{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New York Stock Exhange Predictions RNN-LSTM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Best on Kaggle.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['prices.csv', 'securities.csv', 'prices-split-adjusted.csv', 'fundamentals.csv']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "print(os.listdir(\"../input\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-05 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>123.430000</td>\n",
       "      <td>125.839996</td>\n",
       "      <td>122.309998</td>\n",
       "      <td>126.250000</td>\n",
       "      <td>2163600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-06 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>125.239998</td>\n",
       "      <td>119.980003</td>\n",
       "      <td>119.940002</td>\n",
       "      <td>125.540001</td>\n",
       "      <td>2386400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-07 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>116.379997</td>\n",
       "      <td>114.949997</td>\n",
       "      <td>114.930000</td>\n",
       "      <td>119.739998</td>\n",
       "      <td>2489500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-08 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>115.480003</td>\n",
       "      <td>116.620003</td>\n",
       "      <td>113.500000</td>\n",
       "      <td>117.440002</td>\n",
       "      <td>2006300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-11 00:00:00</td>\n",
       "      <td>WLTW</td>\n",
       "      <td>117.010002</td>\n",
       "      <td>114.970001</td>\n",
       "      <td>114.089996</td>\n",
       "      <td>117.330002</td>\n",
       "      <td>1408600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  date symbol        open       close         low        high  \\\n",
       "0  2016-01-05 00:00:00   WLTW  123.430000  125.839996  122.309998  126.250000   \n",
       "1  2016-01-06 00:00:00   WLTW  125.239998  119.980003  119.940002  125.540001   \n",
       "2  2016-01-07 00:00:00   WLTW  116.379997  114.949997  114.930000  119.739998   \n",
       "3  2016-01-08 00:00:00   WLTW  115.480003  116.620003  113.500000  117.440002   \n",
       "4  2016-01-11 00:00:00   WLTW  117.010002  114.970001  114.089996  117.330002   \n",
       "\n",
       "      volume  \n",
       "0  2163600.0  \n",
       "1  2386400.0  \n",
       "2  2489500.0  \n",
       "3  2006300.0  \n",
       "4  1408600.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df =  pd.read_csv('../input/prices.csv', header=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "218ec1759efe8472d2b316d320dc06e98efe1930"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(851264, 7)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "2db426eb4902a4f41f940bb1198575b62d5de5b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['WLTW', 'A', 'AAL', 'AAP', 'AAPL', 'ABC', 'ABT', 'ACN', 'ADBE',\n",
       "       'ADI', 'ADM', 'ADP', 'ADS', 'ADSK', 'AEE', 'AEP', 'AES', 'AET',\n",
       "       'AFL', 'AGN'], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.symbol.unique()[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "aa98c697c1cc1b43ee10f7fc0e18386d2958ed75"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "851264\n"
     ]
    }
   ],
   "source": [
    "print(len(df.symbol.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "a9f0e53afb3aa44b63d3d5b86c6eb15212b1191d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>851264.000000</td>\n",
       "      <td>851264.000000</td>\n",
       "      <td>851264.000000</td>\n",
       "      <td>851264.000000</td>\n",
       "      <td>8.512640e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>70.836986</td>\n",
       "      <td>70.857109</td>\n",
       "      <td>70.118414</td>\n",
       "      <td>71.543476</td>\n",
       "      <td>5.415113e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>83.695876</td>\n",
       "      <td>83.689686</td>\n",
       "      <td>82.877294</td>\n",
       "      <td>84.465504</td>\n",
       "      <td>1.249468e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.850000</td>\n",
       "      <td>0.860000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.880000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.840000</td>\n",
       "      <td>33.849998</td>\n",
       "      <td>33.480000</td>\n",
       "      <td>34.189999</td>\n",
       "      <td>1.221500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.770000</td>\n",
       "      <td>52.799999</td>\n",
       "      <td>52.230000</td>\n",
       "      <td>53.310001</td>\n",
       "      <td>2.476250e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>79.879997</td>\n",
       "      <td>79.889999</td>\n",
       "      <td>79.110001</td>\n",
       "      <td>80.610001</td>\n",
       "      <td>5.222500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1584.439941</td>\n",
       "      <td>1578.130005</td>\n",
       "      <td>1549.939941</td>\n",
       "      <td>1600.930054</td>\n",
       "      <td>8.596434e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                open          close            low           high  \\\n",
       "count  851264.000000  851264.000000  851264.000000  851264.000000   \n",
       "mean       70.836986      70.857109      70.118414      71.543476   \n",
       "std        83.695876      83.689686      82.877294      84.465504   \n",
       "min         0.850000       0.860000       0.830000       0.880000   \n",
       "25%        33.840000      33.849998      33.480000      34.189999   \n",
       "50%        52.770000      52.799999      52.230000      53.310001   \n",
       "75%        79.879997      79.889999      79.110001      80.610001   \n",
       "max      1584.439941    1578.130005    1549.939941    1600.930054   \n",
       "\n",
       "             volume  \n",
       "count  8.512640e+05  \n",
       "mean   5.415113e+06  \n",
       "std    1.249468e+07  \n",
       "min    0.000000e+00  \n",
       "25%    1.221500e+06  \n",
       "50%    2.476250e+06  \n",
       "75%    5.222500e+06  \n",
       "max    8.596434e+08  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "45a1e90e9a7eba6cffb0593603920403ecdb7e1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      0\n",
       "symbol    0\n",
       "open      0\n",
       "close     0\n",
       "low       0\n",
       "high      0\n",
       "volume    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "d5b727df06a7696e894552d48c346e8b1aecc151"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2016-01-05 00:00:00', '2016-01-06 00:00:00',\n",
       "       '2016-01-07 00:00:00', ..., '2016-12-28', '2016-12-29',\n",
       "       '2016-12-30'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "92e453fb9e605aa2bc4060c7e6db67c28731d232"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub Industry</th>\n",
       "      <th>Address of Headquarters</th>\n",
       "      <th>Date first added</th>\n",
       "      <th>CIK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>3M Company</td>\n",
       "      <td>reports</td>\n",
       "      <td>Industrials</td>\n",
       "      <td>Industrial Conglomerates</td>\n",
       "      <td>St. Paul, Minnesota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABT</td>\n",
       "      <td>Abbott Laboratories</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Health Care Equipment</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>1964-03-31</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>AbbVie</td>\n",
       "      <td>reports</td>\n",
       "      <td>Health Care</td>\n",
       "      <td>Pharmaceuticals</td>\n",
       "      <td>North Chicago, Illinois</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1551152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACN</td>\n",
       "      <td>Accenture plc</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>IT Consulting &amp; Other Services</td>\n",
       "      <td>Dublin, Ireland</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>1467373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ATVI</td>\n",
       "      <td>Activision Blizzard</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Home Entertainment Software</td>\n",
       "      <td>Santa Monica, California</td>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>718877</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ticker symbol             Security SEC filings             GICS Sector  \\\n",
       "0           MMM           3M Company     reports             Industrials   \n",
       "1           ABT  Abbott Laboratories     reports             Health Care   \n",
       "2          ABBV               AbbVie     reports             Health Care   \n",
       "3           ACN        Accenture plc     reports  Information Technology   \n",
       "4          ATVI  Activision Blizzard     reports  Information Technology   \n",
       "\n",
       "                GICS Sub Industry   Address of Headquarters Date first added  \\\n",
       "0        Industrial Conglomerates       St. Paul, Minnesota              NaN   \n",
       "1           Health Care Equipment   North Chicago, Illinois       1964-03-31   \n",
       "2                 Pharmaceuticals   North Chicago, Illinois       2012-12-31   \n",
       "3  IT Consulting & Other Services           Dublin, Ireland       2011-07-06   \n",
       "4     Home Entertainment Software  Santa Monica, California       2015-08-31   \n",
       "\n",
       "       CIK  \n",
       "0    66740  \n",
       "1     1800  \n",
       "2  1551152  \n",
       "3  1467373  \n",
       "4   718877  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_info = pd.read_csv('../input/securities.csv')\n",
    "comp_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "02bb4b2fdb15281746cfacd76899b2585bb5676a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "505"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_info[\"Ticker symbol\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "c00023da2dd9c9924abf1968c701549746b3b333"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker symbol</th>\n",
       "      <th>Security</th>\n",
       "      <th>SEC filings</th>\n",
       "      <th>GICS Sector</th>\n",
       "      <th>GICS Sub Industry</th>\n",
       "      <th>Address of Headquarters</th>\n",
       "      <th>Date first added</th>\n",
       "      <th>CIK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>FB</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>reports</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>Internet Software &amp; Services</td>\n",
       "      <td>Menlo Park, California</td>\n",
       "      <td>2013-12-23</td>\n",
       "      <td>1326801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Ticker symbol  Security SEC filings             GICS Sector  \\\n",
       "181            FB  Facebook     reports  Information Technology   \n",
       "\n",
       "                GICS Sub Industry Address of Headquarters Date first added  \\\n",
       "181  Internet Software & Services  Menlo Park, California       2013-12-23   \n",
       "\n",
       "         CIK  \n",
       "181  1326801  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_info.loc[comp_info.Security.str.startswith('Face') , :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Selecting any 6 companies using above method for visualizations on respective opening and closing stock prices.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "34d383257979ef5f95c4741043e477201b14c31a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6      ADBE\n",
      "181      FB\n",
      "212      GS\n",
      "306    MSFT\n",
      "496     XRX\n",
      "500    YHOO\n",
      "Name: Ticker symbol, dtype: object\n"
     ]
    }
   ],
   "source": [
    "comp_plot = comp_info.loc[(comp_info[\"Security\"] == 'Yahoo Inc.') | (comp_info[\"Security\"] == 'Xerox Corp.') | (comp_info[\"Security\"] == 'Adobe Systems Inc')\n",
    "              | (comp_info[\"Security\"] == 'Microsoft Corp.') | (comp_info[\"Security\"] == 'Adobe Systems Inc') \n",
    "              | (comp_info[\"Security\"] == 'Facebook') | (comp_info[\"Security\"] == 'Goldman Sachs Group') , [\"Ticker symbol\"] ][\"Ticker symbol\"] \n",
    "print(comp_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "317c78f50c1e0eac990d250def86f61697c651b7"
   },
   "source": [
    "**Some companies and their opening and closing stock prices.**\n",
    "**Note: The differences is marginal , you might take open and close same but they are actually different after looking carefully.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "ad8683f971dfc70d3626be60500632b7b75e270a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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e9cmmRF2Rr7kmkfb993DnnWFfq6RJTctmLvqfA78CbnL3mWa2DWGNeBGRWld6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\nYTNnhu3nn4dazxVZlHDePPj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3s0vq+/dpZo+Y2QIz+zQprdLfnZmdGV0/zczOTPdZtamc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f641be9a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dt6RzUzP7nZl9Z2YTzOwlM2tgZruY2Sgzm2ZmL5uZXgOIVCHvvgs//pi5623e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\nasCsa/hwdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f641bb8a080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zlpfNP2Tf/76z/r3yyli3uX/9K/mxVXWjWLHCPfQ+9Wt5RF/E550X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d7D8Iw/v5JzEJFPgV+BriKSJiIVlQJaAOwCUjCK29zqhVy1AnviBneFYT7e8DEAHRp3cHu++YMd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f640b37cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gZs6cQ0nJgrh1o3GCvw8//jiPkpIfEpZdv74rP//clKlTl9OoURs+//xLdt/d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kcKDt53kPX/XF9mWqWUakxq5B25iHQE+gC/A4OA20XkGmAZVq/dY+9JERkHjANISEggKSnJb+XJ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\nM7MCcyI3MzMrsP8D2rd5SNhz2mAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f640b1c7518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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shWTkyJF5nT99une1cZ9i8u237hrnnaf685+rNjXFPzfcz003TW4zqPbvH33e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\nu3ctJVUl5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SVyY1KA/77xpkzjaowx1tnNmBSx9u61/L3q70FXwxgTMpbIjUkRI3qNCLoK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\nQa8msAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f640afe3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4Dvh1apNljMlV8db39ktxffvGbuMOO/VUV61d0cQk8YL4n/7kAovvb3+r+r3y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\nyhSzr5RSIUAzjIC7y8WBaF2AvkqpG4AwIAqjZx6tlAqy9Mb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1MrA1qcvlNS53a928VXhQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f640b19d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plotter(code):\n",
    "    global closing_stock\n",
    "    global opening_stock\n",
    "    f, axs = plt.subplots(2,2,figsize=(8,8))\n",
    "    plt.subplot(212)\n",
    "    company = df[df['symbol']==code]\n",
    "    company = company.open.values.astype('float32')\n",
    "    company = company.reshape(-1, 1)\n",
    "    opening_stock = company\n",
    "    plt.grid(True)\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel(code + \" open stock prices\")\n",
    "    plt.title('prices Vs Time')\n",
    "    plt.plot(company , 'g')\n",
    "    \n",
    "    plt.subplot(211)\n",
    "    company_close = df[df['symbol']==code]\n",
    "    company_close = company_close.close.values.astype('float32')\n",
    "    company_close = company_close.reshape(-1, 1)\n",
    "    closing_stock = company_close\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel(code + \" close stock prices\")\n",
    "    plt.title('prices Vs Time')\n",
    "    plt.grid(True)\n",
    "    plt.plot(company_close , 'b')\n",
    "    plt.show()\n",
    "for i in comp_plot:\n",
    "    plotter(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7fff78b6e9d93e97548016451cc84223a1433eff"
   },
   "source": [
    "**Lets take a single stock as a sample to forecast further stock prices.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "ca105e9fdd3ef59a2cad9fbe09140a71b788c057"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([17.1 , 17.23, 17.17, ..., 38.73, 38.64, 38.67], dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closing_stock[: , 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "50b950b5b6f8f44e538229e35dd0d2de055c8e39"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17.1  17.23 17.17 ... 38.73 38.64 38.67]\n"
     ]
    }
   ],
   "source": [
    "stocks = closing_stock[: , 0]\n",
    "print(stocks)\n",
    "stocks = stocks.reshape(len(stocks) , 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Feature scaling the vector for better model performance.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "f4a41a09ed193ec858120fd83f6bf1c0a47f737d",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "stocks = scaler.fit_transform(stocks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "62c6b9de14738b7bb41999bcbd2cf2cd36eedb8e",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = int(len(stocks) * 0.80)\n",
    "test = len(stocks) - train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "50a3196f0c077fac6fd83115e9c025255f134c1b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1409 353\n"
     ]
    }
   ],
   "source": [
    "print(train , test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "7c2d615c6e78b9be8fd737ee68f659089c237bdb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.14559111]\n",
      " [0.14874032]\n",
      " [0.14728683]\n",
      " ...\n",
      " [0.63372093]\n",
      " [0.6145833 ]\n",
      " [0.6196705 ]]\n"
     ]
    }
   ],
   "source": [
    "train = stocks[0:train]\n",
    "print(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "557f2292078bb03a0e3adeed3a6e9a1236a7f62a",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = stocks[len(train) : ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_uuid": "5999f4fd2510a257417ddf13a39a7c1bfa7f5081",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train = train.reshape(len(train) , 1)\n",
    "test = test.reshape(len(test) , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_uuid": "0d4de6c4733e6d6426322faaa84f7fd8c32c3b11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1409, 1) (353, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape , test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**process_data for the required input for LSTM.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_uuid": "918b4302db6a2764ab77350d38493b427ddfb414",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_data(data , n_features):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(data)-n_features-1):\n",
    "        a = data[i:(i+n_features), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(data[i + n_features, 0])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here I am taking 2 past values to predict a single value. This has already been checked and optimized after taking several cases.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_uuid": "19a7f217f4aed70ff5660a79f55514670dad8f60",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_features = 2\n",
    "\n",
    "trainX, trainY = process_data(train, n_features)\n",
    "testX, testY = process_data(test, n_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_uuid": "8af00b0e91ca09e51cf5728dfbc847ca3702d4df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1406, 2) (1406,) (350, 2) (350,)\n"
     ]
    }
   ],
   "source": [
    "print(trainX.shape , trainY.shape , testX.shape , testY.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reshaping again for required LSTM input as (sample , timestamp , features per sample).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here I am taking timestamp as 1.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_uuid": "f6b3128ecf8c275b7e22892adbe4c8ca2edcb19e",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainX = trainX.reshape(trainX.shape[0] , 1 ,trainX.shape[1])\n",
    "testX = testX.reshape(testX.shape[0] , 1 ,testX.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_uuid": "01c89adb2d29b141617e323c08419637e761decd"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas\n",
    "import math\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense , BatchNormalization , Dropout , Activation\n",
    "from keras.layers import LSTM , GRU\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.optimizers import Adam , SGD , RMSprop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Checkpointing the model when required and using other callbacks.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_uuid": "51aafba6b33ebea5e345029dc30c8bfffdaa84c4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/Keras-2.1.5-py3.6.egg/keras/callbacks.py:919: UserWarning: `epsilon` argument is deprecated and will be removed, use `min_delta` insted.\n"
     ]
    }
   ],
   "source": [
    "filepath=\"stock_weights.hdf5\"\n",
    "from keras.callbacks import ReduceLROnPlateau , ModelCheckpoint\n",
    "lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.1, epsilon=0.0001, patience=1, verbose=1)\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='max')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## My personal tuned architecture for best performance on kaggle till date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The learning rate has been tuned for several times and so is the batch_size and the neurons in the layers.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_uuid": "d6cea41f648af87a6f67c7cefcdebb3a14f3fdbc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (None, 1, 256)            198912    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1, 256)            0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 256)               525312    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 740,737\n",
      "Trainable params: 740,737\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(GRU(256 , input_shape = (1 , n_features) , return_sequences=True))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(LSTM(256))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(64 ,  activation = 'relu'))\n",
    "model.add(Dense(1))\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_uuid": "e8b1fe329e5814406236d34cc783b2403398e0c4",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.compile(loss='mean_squared_error', optimizer=Adam(lr = 0.0005) , metrics = ['mean_squared_error'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_uuid": "efb8e8babaaac9f4bfa944045545d1090d9b8e98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1406 samples, validate on 350 samples\n",
      "Epoch 1/100\n",
      "1406/1406 [==============================] - 2s 1ms/step - loss: 8.5730e-04 - mean_squared_error: 8.5730e-04 - val_loss: 3.4262e-04 - val_mean_squared_error: 3.4262e-04\n",
      "\n",
      "Epoch 00001: val_loss did not improve\n",
      "Epoch 2/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.6215e-04 - mean_squared_error: 7.6215e-04 - val_loss: 4.1608e-04 - val_mean_squared_error: 4.1608e-04\n",
      "\n",
      "Epoch 00002: val_loss did not improve\n",
      "\n",
      "Epoch 00002: ReduceLROnPlateau reducing learning rate to 5.0000002374872565e-05.\n",
      "Epoch 3/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 8.4767e-04 - mean_squared_error: 8.4767e-04 - val_loss: 3.3152e-04 - val_mean_squared_error: 3.3152e-04\n",
      "\n",
      "Epoch 00003: val_loss did not improve\n",
      "\n",
      "Epoch 00003: ReduceLROnPlateau reducing learning rate to 5.000000237487257e-06.\n",
      "Epoch 4/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.2755e-04 - mean_squared_error: 7.2755e-04 - val_loss: 3.3100e-04 - val_mean_squared_error: 3.3100e-04\n",
      "\n",
      "Epoch 00004: val_loss did not improve\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 5.000000328436726e-07.\n",
      "Epoch 5/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 8.0369e-04 - mean_squared_error: 8.0369e-04 - val_loss: 3.2886e-04 - val_mean_squared_error: 3.2886e-04\n",
      "\n",
      "Epoch 00005: val_loss did not improve\n",
      "\n",
      "Epoch 00005: ReduceLROnPlateau reducing learning rate to 5.000000555810402e-08.\n",
      "Epoch 6/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.8124e-04 - mean_squared_error: 7.8124e-04 - val_loss: 3.2861e-04 - val_mean_squared_error: 3.2861e-04\n",
      "\n",
      "Epoch 00006: val_loss did not improve\n",
      "\n",
      "Epoch 00006: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-09.\n",
      "Epoch 7/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.0411e-04 - mean_squared_error: 7.0411e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00007: val_loss did not improve\n",
      "\n",
      "Epoch 00007: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-10.\n",
      "Epoch 8/100\n",
      "1406/1406 [==============================] - 0s 94us/step - loss: 8.2718e-04 - mean_squared_error: 8.2718e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00008: val_loss did not improve\n",
      "\n",
      "Epoch 00008: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-11.\n",
      "Epoch 9/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.5324e-04 - mean_squared_error: 7.5324e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00009: val_loss did not improve\n",
      "\n",
      "Epoch 00009: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-12.\n",
      "Epoch 10/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.2990e-04 - mean_squared_error: 7.2990e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00010: val_loss did not improve\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 5.000000413701855e-13.\n",
      "Epoch 11/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.9096e-04 - mean_squared_error: 7.9096e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00011: val_loss did not improve\n",
      "\n",
      "Epoch 00011: ReduceLROnPlateau reducing learning rate to 5.0000005221220725e-14.\n",
      "Epoch 12/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.4610e-04 - mean_squared_error: 7.4610e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00012: val_loss did not improve\n",
      "\n",
      "Epoch 00012: ReduceLROnPlateau reducing learning rate to 5.000000589884709e-15.\n",
      "Epoch 13/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 8.3228e-04 - mean_squared_error: 8.3228e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00013: val_loss did not improve\n",
      "\n",
      "Epoch 00013: ReduceLROnPlateau reducing learning rate to 5.000000759291298e-16.\n",
      "Epoch 14/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 8.7445e-04 - mean_squared_error: 8.7445e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00014: val_loss did not improve\n",
      "\n",
      "Epoch 00014: ReduceLROnPlateau reducing learning rate to 5.000000547533061e-17.\n",
      "Epoch 15/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.8557e-04 - mean_squared_error: 7.8557e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00015: val_loss did not improve\n",
      "\n",
      "Epoch 00015: ReduceLROnPlateau reducing learning rate to 5.000000415184163e-18.\n",
      "Epoch 16/100\n",
      "1406/1406 [==============================] - 0s 95us/step - loss: 7.5142e-04 - mean_squared_error: 7.5142e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00016: val_loss did not improve\n",
      "\n",
      "Epoch 00016: ReduceLROnPlateau reducing learning rate to 5.000000332466102e-19.\n",
      "Epoch 17/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.1217e-04 - mean_squared_error: 7.1217e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00017: val_loss did not improve\n",
      "\n",
      "Epoch 00017: ReduceLROnPlateau reducing learning rate to 5.000000229068525e-20.\n",
      "Epoch 18/100\n",
      "1406/1406 [==============================] - 0s 102us/step - loss: 8.2670e-04 - mean_squared_error: 8.2670e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00018: val_loss did not improve\n",
      "\n",
      "Epoch 00018: ReduceLROnPlateau reducing learning rate to 5.00000016444504e-21.\n",
      "Epoch 19/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 6.8475e-04 - mean_squared_error: 6.8475e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00019: val_loss did not improve\n",
      "\n",
      "Epoch 00019: ReduceLROnPlateau reducing learning rate to 5.000000245224397e-22.\n",
      "Epoch 20/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.6283e-04 - mean_squared_error: 7.6283e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00020: val_loss did not improve\n",
      "\n",
      "Epoch 00020: ReduceLROnPlateau reducing learning rate to 5.0000003461985925e-23.\n",
      "Epoch 21/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.7437e-04 - mean_squared_error: 7.7437e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00021: val_loss did not improve\n",
      "\n",
      "Epoch 00021: ReduceLROnPlateau reducing learning rate to 5.000000472416337e-24.\n",
      "Epoch 22/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 8.0855e-04 - mean_squared_error: 8.0855e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00022: val_loss did not improve\n",
      "\n",
      "Epoch 00022: ReduceLROnPlateau reducing learning rate to 5.000000393530247e-25.\n",
      "Epoch 23/100\n",
      "1406/1406 [==============================] - 0s 95us/step - loss: 7.6211e-04 - mean_squared_error: 7.6211e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00023: val_loss did not improve\n",
      "\n",
      "Epoch 00023: ReduceLROnPlateau reducing learning rate to 5.000000590745473e-26.\n",
      "Epoch 24/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 8.1772e-04 - mean_squared_error: 8.1772e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00024: val_loss did not improve\n",
      "\n",
      "Epoch 00024: ReduceLROnPlateau reducing learning rate to 5.000000714004989e-27.\n",
      "Epoch 25/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.5941e-04 - mean_squared_error: 7.5941e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00025: val_loss did not improve\n",
      "\n",
      "Epoch 00025: ReduceLROnPlateau reducing learning rate to 5.00000071400499e-28.\n",
      "Epoch 26/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.3155e-04 - mean_squared_error: 7.3155e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00026: val_loss did not improve\n",
      "\n",
      "Epoch 00026: ReduceLROnPlateau reducing learning rate to 5.000000617708492e-29.\n",
      "Epoch 27/100\n",
      "1406/1406 [==============================] - 0s 100us/step - loss: 8.4913e-04 - mean_squared_error: 8.4913e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00027: val_loss did not improve\n",
      "\n",
      "Epoch 00027: ReduceLROnPlateau reducing learning rate to 5.0000006177084924e-30.\n",
      "Epoch 28/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.7403e-04 - mean_squared_error: 7.7403e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00028: val_loss did not improve\n",
      "\n",
      "Epoch 00028: ReduceLROnPlateau reducing learning rate to 5.0000007681717695e-31.\n",
      "Epoch 29/100\n",
      "1406/1406 [==============================] - 0s 95us/step - loss: 7.3783e-04 - mean_squared_error: 7.3783e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00029: val_loss did not improve\n",
      "\n",
      "Epoch 00029: ReduceLROnPlateau reducing learning rate to 5.000000956250865e-32.\n",
      "Epoch 30/100\n",
      "1406/1406 [==============================] - 0s 103us/step - loss: 7.7587e-04 - mean_squared_error: 7.7587e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00030: val_loss did not improve\n",
      "\n",
      "Epoch 00030: ReduceLROnPlateau reducing learning rate to 5.0000010738003005e-33.\n",
      "Epoch 31/100\n",
      "1406/1406 [==============================] - 0s 103us/step - loss: 7.8871e-04 - mean_squared_error: 7.8871e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00031: val_loss did not improve\n",
      "\n",
      "Epoch 00031: ReduceLROnPlateau reducing learning rate to 5.000001220737094e-34.\n",
      "Epoch 32/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.5369e-04 - mean_squared_error: 7.5369e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00032: val_loss did not improve\n",
      "\n",
      "Epoch 00032: ReduceLROnPlateau reducing learning rate to 5.000001037066102e-35.\n",
      "Epoch 33/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 8.1045e-04 - mean_squared_error: 8.1045e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00033: val_loss did not improve\n",
      "\n",
      "Epoch 00033: ReduceLROnPlateau reducing learning rate to 5.000000807477361e-36.\n",
      "Epoch 34/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.4532e-04 - mean_squared_error: 7.4532e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00034: val_loss did not improve\n",
      "\n",
      "Epoch 00034: ReduceLROnPlateau reducing learning rate to 5.000000807477361e-37.\n",
      "Epoch 35/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 6.8260e-04 - mean_squared_error: 6.8260e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00035: val_loss did not improve\n",
      "\n",
      "Epoch 00035: ReduceLROnPlateau reducing learning rate to 5.000000628111158e-38.\n",
      "Epoch 36/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.2568e-04 - mean_squared_error: 7.2568e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00036: val_loss did not improve\n",
      "\n",
      "Epoch 00036: ReduceLROnPlateau reducing learning rate to 5.000000516007281e-39.\n",
      "Epoch 37/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 8.3018e-04 - mean_squared_error: 8.3018e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00037: val_loss did not improve\n",
      "\n",
      "Epoch 00037: ReduceLROnPlateau reducing learning rate to 5.000001076526666e-40.\n",
      "Epoch 38/100\n",
      "1406/1406 [==============================] - 0s 107us/step - loss: 7.2467e-04 - mean_squared_error: 7.2467e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00038: val_loss did not improve\n",
      "\n",
      "Epoch 00038: ReduceLROnPlateau reducing learning rate to 5.000001076526667e-41.\n",
      "Epoch 39/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.7145e-04 - mean_squared_error: 7.7145e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00039: val_loss did not improve\n",
      "\n",
      "Epoch 00039: ReduceLROnPlateau reducing learning rate to 4.99997305055738e-42.\n",
      "Epoch 40/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.9044e-04 - mean_squared_error: 7.9044e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00040: val_loss did not improve\n",
      "\n",
      "Epoch 00040: ReduceLROnPlateau reducing learning rate to 4.9998329207109475e-43.\n",
      "Epoch 41/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.7556e-04 - mean_squared_error: 7.7556e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00041: val_loss did not improve\n",
      "\n",
      "Epoch 00041: ReduceLROnPlateau reducing learning rate to 5.002635517639597e-44.\n",
      "Epoch 42/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.8930e-04 - mean_squared_error: 7.8930e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00042: val_loss did not improve\n",
      "\n",
      "Epoch 00042: ReduceLROnPlateau reducing learning rate to 5.0446744715693416e-45.\n",
      "Epoch 43/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 8.5694e-04 - mean_squared_error: 8.5694e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00043: val_loss did not improve\n",
      "\n",
      "Epoch 00043: ReduceLROnPlateau reducing learning rate to 5.6051938572992686e-46.\n",
      "Epoch 44/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.6490e-04 - mean_squared_error: 7.6490e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00044: val_loss did not improve\n",
      "Epoch 45/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 8.5439e-04 - mean_squared_error: 8.5439e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00045: val_loss did not improve\n",
      "Epoch 46/100\n",
      "1406/1406 [==============================] - 0s 105us/step - loss: 7.3485e-04 - mean_squared_error: 7.3485e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00046: val_loss did not improve\n",
      "Epoch 47/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.9749e-04 - mean_squared_error: 7.9749e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00047: val_loss did not improve\n",
      "Epoch 48/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.9004e-04 - mean_squared_error: 7.9004e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00048: val_loss did not improve\n",
      "Epoch 49/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.9713e-04 - mean_squared_error: 7.9713e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00049: val_loss did not improve\n",
      "Epoch 50/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 8.1592e-04 - mean_squared_error: 8.1592e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00050: val_loss did not improve\n",
      "Epoch 51/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 6.9509e-04 - mean_squared_error: 6.9509e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00051: val_loss did not improve\n",
      "Epoch 52/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.9954e-04 - mean_squared_error: 7.9954e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00052: val_loss did not improve\n",
      "Epoch 53/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.8145e-04 - mean_squared_error: 7.8145e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00053: val_loss did not improve\n",
      "Epoch 54/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.8603e-04 - mean_squared_error: 7.8603e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00054: val_loss did not improve\n",
      "Epoch 55/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 8.4701e-04 - mean_squared_error: 8.4701e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00055: val_loss did not improve\n",
      "Epoch 56/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.8612e-04 - mean_squared_error: 7.8612e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00056: val_loss did not improve\n",
      "Epoch 57/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.5024e-04 - mean_squared_error: 7.5024e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00057: val_loss did not improve\n",
      "Epoch 58/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 8.0423e-04 - mean_squared_error: 8.0423e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00058: val_loss did not improve\n",
      "Epoch 59/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.5330e-04 - mean_squared_error: 7.5330e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00059: val_loss did not improve\n",
      "Epoch 60/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.0811e-04 - mean_squared_error: 7.0811e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00060: val_loss did not improve\n",
      "Epoch 61/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.6382e-04 - mean_squared_error: 7.6382e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00061: val_loss did not improve\n",
      "Epoch 62/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.9563e-04 - mean_squared_error: 7.9563e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00062: val_loss did not improve\n",
      "Epoch 63/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.4980e-04 - mean_squared_error: 7.4980e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00063: val_loss did not improve\n",
      "Epoch 64/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.8338e-04 - mean_squared_error: 7.8338e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00064: val_loss did not improve\n",
      "Epoch 65/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.2500e-04 - mean_squared_error: 7.2500e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00065: val_loss did not improve\n",
      "Epoch 66/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.7823e-04 - mean_squared_error: 7.7823e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00066: val_loss did not improve\n",
      "Epoch 67/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.3227e-04 - mean_squared_error: 7.3227e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00067: val_loss did not improve\n",
      "Epoch 68/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 6.8531e-04 - mean_squared_error: 6.8531e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00068: val_loss did not improve\n",
      "Epoch 69/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 8.0679e-04 - mean_squared_error: 8.0679e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00069: val_loss did not improve\n",
      "Epoch 70/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.2756e-04 - mean_squared_error: 7.2756e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00070: val_loss did not improve\n",
      "Epoch 71/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 8.2163e-04 - mean_squared_error: 8.2163e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00071: val_loss did not improve\n",
      "Epoch 72/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 8.1969e-04 - mean_squared_error: 8.1969e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00072: val_loss did not improve\n",
      "Epoch 73/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.2178e-04 - mean_squared_error: 7.2178e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00073: val_loss did not improve\n",
      "Epoch 74/100\n",
      "1406/1406 [==============================] - 0s 100us/step - loss: 7.9577e-04 - mean_squared_error: 7.9577e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00074: val_loss did not improve\n",
      "Epoch 75/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 8.2726e-04 - mean_squared_error: 8.2726e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00075: val_loss did not improve\n",
      "Epoch 76/100\n",
      "1406/1406 [==============================] - 0s 97us/step - loss: 7.1044e-04 - mean_squared_error: 7.1044e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00076: val_loss did not improve\n",
      "Epoch 77/100\n",
      "1406/1406 [==============================] - 0s 100us/step - loss: 7.0231e-04 - mean_squared_error: 7.0231e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00077: val_loss did not improve\n",
      "Epoch 78/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 8.9322e-04 - mean_squared_error: 8.9322e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00078: val_loss did not improve\n",
      "Epoch 79/100\n",
      "1406/1406 [==============================] - 0s 101us/step - loss: 8.0106e-04 - mean_squared_error: 8.0106e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00079: val_loss did not improve\n",
      "Epoch 80/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.6759e-04 - mean_squared_error: 7.6759e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00080: val_loss did not improve\n",
      "Epoch 81/100\n",
      "1406/1406 [==============================] - 0s 100us/step - loss: 8.5060e-04 - mean_squared_error: 8.5060e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00081: val_loss did not improve\n",
      "Epoch 82/100\n",
      "1406/1406 [==============================] - 0s 100us/step - loss: 8.0596e-04 - mean_squared_error: 8.0596e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00082: val_loss did not improve\n",
      "Epoch 83/100\n",
      "1406/1406 [==============================] - 0s 100us/step - loss: 8.5428e-04 - mean_squared_error: 8.5428e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00083: val_loss did not improve\n",
      "Epoch 84/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.9592e-04 - mean_squared_error: 7.9592e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00084: val_loss did not improve\n",
      "Epoch 85/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 8.0217e-04 - mean_squared_error: 8.0217e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00085: val_loss did not improve\n",
      "Epoch 86/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 8.0354e-04 - mean_squared_error: 8.0354e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00086: val_loss did not improve\n",
      "Epoch 87/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.8705e-04 - mean_squared_error: 7.8705e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00087: val_loss did not improve\n",
      "Epoch 88/100\n",
      "1406/1406 [==============================] - 0s 102us/step - loss: 7.8920e-04 - mean_squared_error: 7.8920e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00088: val_loss did not improve\n",
      "Epoch 89/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 8.0076e-04 - mean_squared_error: 8.0076e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00089: val_loss did not improve\n",
      "Epoch 90/100\n",
      "1406/1406 [==============================] - 0s 100us/step - loss: 7.9664e-04 - mean_squared_error: 7.9664e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00090: val_loss did not improve\n",
      "Epoch 91/100\n",
      "1406/1406 [==============================] - 0s 100us/step - loss: 7.2140e-04 - mean_squared_error: 7.2140e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00091: val_loss did not improve\n",
      "Epoch 92/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 8.3153e-04 - mean_squared_error: 8.3153e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00092: val_loss did not improve\n",
      "Epoch 93/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 6.9638e-04 - mean_squared_error: 6.9638e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00093: val_loss did not improve\n",
      "Epoch 94/100\n",
      "1406/1406 [==============================] - 0s 98us/step - loss: 6.7735e-04 - mean_squared_error: 6.7735e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00094: val_loss did not improve\n",
      "Epoch 95/100\n",
      "1406/1406 [==============================] - 0s 102us/step - loss: 7.7817e-04 - mean_squared_error: 7.7817e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00095: val_loss did not improve\n",
      "Epoch 96/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1406/1406 [==============================] - 0s 98us/step - loss: 7.4128e-04 - mean_squared_error: 7.4128e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00096: val_loss did not improve\n",
      "Epoch 97/100\n",
      "1406/1406 [==============================] - 0s 105us/step - loss: 8.2424e-04 - mean_squared_error: 8.2424e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00097: val_loss did not improve\n",
      "Epoch 98/100\n",
      "1406/1406 [==============================] - 0s 99us/step - loss: 7.1726e-04 - mean_squared_error: 7.1726e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00098: val_loss did not improve\n",
      "Epoch 99/100\n",
      "1406/1406 [==============================] - 0s 96us/step - loss: 7.0614e-04 - mean_squared_error: 7.0614e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00099: val_loss did not improve\n",
      "Epoch 100/100\n",
      "1406/1406 [==============================] - 0s 103us/step - loss: 7.4282e-04 - mean_squared_error: 7.4282e-04 - val_loss: 3.2859e-04 - val_mean_squared_error: 3.2859e-04\n",
      "\n",
      "Epoch 00100: val_loss did not improve\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(trainX, trainY, epochs=100 , batch_size = 128 , \n",
    "          callbacks = [checkpoint , lr_reduce] , validation_data = (testX,testY))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_uuid": "1184c60090c9815a695d2b1748a1b824813528d2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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L6gLOBBbWzbMQOCd/fhpwa0SEpDl5Zz2S9iXrVF8eEU8Az0k6Ou9LeQ/wow7u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\nZmY2Jg4SMzMbk/8fS5l9Ng/2eMoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63e65971d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5FlWLGknqGomZWUdlNm0tAh5pej1QDJsUSacCNeChpsF/WzR5fVxST4f5LpW0RtKajRs3\nTna1wOhRW6lvbGVmNo4yg0RthsWkFiAdDnwVeFdEjNZargB+DzgFOAT4YLt5I+KaiFgeEcsXLlw4\nmdWOGT2PJBwkZmYdlRkkA8CRTa8XAxu6nVnSgcD3gL+KiNtGh0fEY5EbAr5I3oRWimpxZjtu2jIz\n66jMIFkNHCNpqaQacDGwopsZi+lvBL4SEf/UMu7w4q+A84F7prXUTXqKGombtszMOistSCKiDlwG\nrALuB66PiHslXSXpXABJp0gaAN4EfFbSvcXsFwKvAN7Z5jDfr0v6JfBLYAHwkbK2YfRaWw4SM7PO\nSj0hMSJWAitbhl3Z9Hw1eZNX63xfA77WYZmvnuZidpQkIiOFrPFsrdLM7DnHl0iZQKYUhWskZmad\nOEgmkMl9JGZm43GQTCBTBYWbtszMOnGQTCCUIveRmJl15CCZQLiPxMxsXA6SCURSIXHTlplZRw6S\nCeQ1EgeJmVknDpIJuEZiZjY+B8lEktRBYmY2DgfJBEKukZiZjcdBMgElFRIcJGZmnThIJpKmpK6R\nmJl15CCZiGskZmbjcpBMJKmQOkjMzDpykExADhIzs3E5SCagtEKFDGJSt5s3M9tvOEgmoKS495cv\n3Ghm1paDZAJJWgUgspEZLomZ2b7JQTIBpXmNpF53kJiZtVNqkEg6W9IDktZKurzN+FdIuktSXdIF\nLeMukfRg8bikafjJkn5ZLPNTklTqNhRBMjzsIDEza6e0IJGUAlcD5wDHAW+WdFzLZL8B3gl8o2Xe\nQ4C/Bl4KnAr8taSDi9GfBi4FjikeZ5e0CcDupq36yHCZqzEze84qs0ZyKrA2ItZFxDBwHXBe8wQR\nsT4ifgFkLfO+BvhBRGyOiC3AD4CzJR0OHBgRP4uIAL4CnF/iNpAUNZKREddIzMzaKTNIFgGPNL0e\nKIZNZd5FxfO9WeZeSSqjTVuukZiZtVNmkLTru+j2ZIxO83a9TEmXSlojac3GjRu7XO2e0tGmLXe2\nm5m1VWaQDABHNr1eDGyY4rwDxfMJlxkR10TE8ohYvnDhwq4L3Sqp5EEyUneNxMysnTKDZDVwjKSl\nkmrAxcCKLuddBZwl6eCik/0sYFVEPAZsl3RacbTWO4DvllH4Ueno4b/uIzEza6u0IImIOnAZeSjc\nD1wfEfdKukrSuQCSTpE0ALwJ+Kyke4t5NwP/lTyMVgNXFcMA/gj4PLAWeAj4flnbALtrJG7aMjNr\nr1LmwiNiJbCyZdiVTc9X88ymqubprgWubTN8DXDC9Ja0s9E+Eh+1ZWbWns9sn0ClmgdJw0FiZtaW\ng2QCPmrLzGx8DpIJjNVI6vUZLomZ2b6pqyCR9KeSDlTuC8X1sc4qu3D7gkrR2d5o+PBfM7N2uq2R\n/O8R8RT5YbgLgXcBHy2tVPuQ3TUSN22ZmbXTbZCMnlH+WuCLEfHvtD/LfNZJi0ukuGnLzKy9boPk\nTkk3kQfJKklz2fNCi7NSrVIDHCRmZp10ex7Ju4FlwLqI2Flc5v1d5RVr31GpjQaJm7bMzNrptkZy\nOvBARGyV9Dbgr4Bt5RVr31Epmrai4SAxM2un2yD5NLBT0ouB/wL8mvxeILNeZbRpq+GmLTOzdroN\nknpxI6nzgE9GxCeBueUVax+SpABkrpGYmbXVbR/JdklXAG8HXl7cRrdaXrH2Iclo05ZrJGZm7XRb\nI7kIGCI/n+S35Hcl/H9LK9W+pAiSzEFiZtZWV0FShMfXgXmSXg8MRsR+0UfiGomZ2fi6vUTKhcAd\n5PcNuRC4XdIFZRZsnzFWI3EfiZlZO932kfzfwCkR8QSApIXAzcANZRVsn1F0tpO5RmJm1k63fSTJ\naIgUNk1i3uc2N22ZmY2r2xrJ/5K0Cvhm8foiWu58OGuNBolrJGZmbXUVJBHxAUlvBF5GfrHGayLi\nxlJLtq8oggTXSMzM2ur6nu0R8W3g2yWWZd9U9JG4RmJm1t64/RyStkt6qs1ju6SnJlq4pLMlPSBp\nraTL24zvkfStYvztkpYUw98q6e6mRyZpWTHu1mKZo+MO3btN75JEgwSyRqmrMTN7rhq3RhIRe30Z\nlOLs96uBM4EBYLWkFRFxX9Nk7wa2RMQLJF0MfAy4KCK+Tn7eCpJOBL4bEXc3zffWiFizt2WbrEyp\nj9oyM+ugzCOvTgXWRsS6iBgGriO/Vlez84AvF89vAM6Q1HrDrDezu5N/RmSkyEFiZtZWmUGyCHik\n6fVAMaztNBFRJ780/fyWaS5izyD5YtGs9aE2wQOApEslrZG0ZuPGjXu7DQBkqkA4SMzM2ikzSNp9\nwcdkppH0UmBnRNzTNP6tEXEi8PLi8fZ2K4+IayJieUQsX7hw4eRK3iJTitxHYmbWVplBMgAc2fR6\nMbCh0zSSKsA8YHPT+ItpqY1ExKPF3+3AN8ib0EoVSl0jMTProMwgWQ0cI2mppBp5KKxomWYFcEnx\n/ALgR8V9T5CUkF/b67rRiSVVJC0onleB1wP3UDLXSMzMOuv6PJLJioi6pMuAVUAKXBsR90q6ClgT\nESuALwBflbSWvCZycdMiXgEMRMS6pmE9wKoiRFLy6319rqxtGNuWpILCQWJm1k5pQQIQEStpuZRK\nRFzZ9HyQvNbRbt5bgdNahu0ATp72gk4glJLSoJEFadK2b9/MbL+1f1x4cYpCFSo0GK5nM10UM7N9\njoOkG0lKSuYgMTNrw0HShUjyGslQw/0kZmatHCTdKGokI43W02DMzMxB0o3EfSRmZp04SLqRVEgd\nJGZmbTlIupFUqChjpOEgMTNr5SDpgooayZBrJGZme3CQdCNNqfjwXzOzthwkXVBSpULdTVtmZm04\nSLqgtOIaiZlZBw6SLigtjtpyjcTMbA8Oki7kne2ukZiZteMg6UKSFickukZiZrYHB0kXkrRKKtdI\nzMzacZB0YaxG4iAxM9uDg6QLSaVKSsOH/5qZteEg6ULiw3/NzDpykHQh8eG/ZmYdlRokks6W9ICk\ntZIubzO+R9K3ivG3S1pSDF8iaZeku4vHZ5rmOVnSL4t5PiWp9JuoKylqJA4SM7M9lBYkklLgauAc\n4DjgzZKOa5ns3cCWiHgB8HHgY03jHoqIZcXjfU3DPw1cChxTPM4uaxvG+H4kZmYdlVkjORVYGxHr\nImIYuA44r2Wa84AvF89vAM4Yr4Yh6XDgwIj4WUQE8BXg/Okveouk4sN/zcw6KDNIFgGPNL0eKIa1\nnSYi6sA2YH4xbqmkn0v6saSXN00/MMEyAZB0qaQ1ktZs3LhxaltSnNk+MlKf2nLMzGahMoOkXc2i\n9abnnaZ5DDgqIk4C/hz4hqQDu1xmPjDimohYHhHLFy5cOIlit5FUAKg3HCRmZq3KDJIB4Mim14uB\nDZ2mkVQB5gGbI2IoIjYBRMSdwEPAC4vpF0+wzOmXpAA06iOlr8rM7LmmzCBZDRwjaamkGnAxsKJl\nmhXAJcXzC4AfRURIWlh01iPp+eSd6usi4jFgu6TTir6UdwDfLXEbcqM1EgeJmdkeKmUtOCLqki4D\nVgEpcG1E3CvpKmBNRKwAvgB8VdJaYDN52AC8ArhKUh1oAO+LiM3FuD8CvgT0Ad8vHuUqgqThpi0z\nsz2UFiQAEbESWNky7Mqm54PAm9rM923g2x2WuQY4YXpLOoHRIBkZflZXa2b2XOAz27tR9JFkdddI\nzMxaOUi6UdRIMjdtmZntwUHSjbE+Ene2m5m1cpB0wzUSM7OOHCTdKPpIdu4anOGCmJntexwk3Shq\nJDsGh9gx5FqJmVkzB0k3iiCpkPHo1l0zXBgzs32Lg6QbRZCkNBjYsnOGC2Nmtm9xkHRjrEbSYGCL\nayRmZs0cJN0oOtt703CQmJm1cJB0o6iRHD634qYtM7MWDpJuFEFy2AFV10jMzFo4SLoxFiQpj2x2\njcTMrJmDpBtFH8nCORW27BzhaZ9LYmY2xkHSjaJGsnBOHiiPunnLzGyMg6QbRZAs6M+DxB3uZma7\nOUi6UQTJ/L7RIHGNxMxslIOkG0Ufydwa9FYT10jMzJo4SLpR1EgUDRYf3O8aiZlZEwdJN4ogIauz\n+OA+B4mZWZNSg0TS2ZIekLRW0uVtxvdI+lYx/nZJS4rhZ0q6U9Ivi7+vbprn1mKZdxePQ8vcBmCP\nIHnETVtmZmMqZS1YUgpcDZwJDACrJa2IiPuaJns3sCUiXiDpYuBjwEXAk8AbImKDpBOAVcCipvne\nGhFryir7HsaCJG/a2rpzhO2DI8ztrT5rRTAz21eVWSM5FVgbEesiYhi4DjivZZrzgC8Xz28AzpCk\niPh5RGwoht8L9ErqKbGs4ys620drJIDvS2JmVigzSBYBjzS9HuCZtYpnTBMRdWAbML9lmjcCP4+I\noaZhXyyatT4kSe1WLulSSWskrdm4ceNUtqOlaas/35jNDhIzMyg3SNp9wcdkppF0PHlz13ubxr81\nIk4EXl483t5u5RFxTUQsj4jlCxcunFTB99DSRwI+KdHMbFSZQTIAHNn0ejGwodM0kirAPGBz8Xox\ncCPwjoh4aHSGiHi0+Lsd+AZ5E1q5mvpI5s+p0VdNfeSWmVmhzCBZDRwjaamkGnAxsKJlmhXAJcXz\nC4AfRURIOgj4HnBFRPzb6MSSKpIWFM+rwOuBe0rchlxTH4kkHwJsZtaktCAp+jwuIz/i6n7g+oi4\nV9JVks4tJvsCMF/SWuDPgdFDhC8DXgB8qOUw3x5glaRfAHcDjwKfK2sbxkigFLL8qr+LD+5jYKub\ntszMoMTDfwEiYiWwsmXYlU3PB4E3tZnvI8BHOiz25OksY9eSyliQHHlIP2vWbyHLgiRp29dvZrbf\n8Jnt3UoqkDUAePHig9g+VOeBx7fPcKHMzGaeg6RbTTWS047Oj1D+2UObZrJEZmb7BAdJt5LdfSSL\nDurjqEP6uW2dg8TMzEHSraYaCcDpz5/P7Q9vJstaT40xM9u/OEi61RIkpx19CNt2jXD/b5+awUKZ\nmc08B0m3mjrbAU57vvtJzMzAQdK9pj4SgMPn9bFkfj+3rds8g4UyM5t5DpJutTRtAZx+9Hxuf3gT\nDfeTmNl+zEHSrTZBctrz57N9sM79j7mfxMz2Xw6SbrX0kYD7SczMwEHSvZY+EoDDDuzl3fPu5DX/\negHUh2eoYGZmM8tB0q02TVsAF1d/zFHDD7Fj3U9noFBmZjPPQdKtdkEytJ2jd/47AHf84IYZKJSZ\n2cxzkHSrTR8JD91Cko2wK53Lgsd/wk8fenJmymZmNoMcJN1q00fCf6yC3nlUfv//5MRkPR+74Sfs\nHN6z+cvMbDZzkHSrtWkry+DBm+DoM6geew4AS7fdwd+t+o8ZKqCZ2cxwkHSrNUgeuxt2PAEvfA08\n78XQv4B3HvYQ1/7bw3z2xw8R4ZMUzWz/4CDpVmsfyX+sAgQvOBOSBI5+FS8eupPXn3gY/+37v+Iv\nb7yHkUY2Y8U1M3u2OEi61dpH8uAqWHwKzMlPSuQFf4B2PsmnXpnyx686mm/e8Rve+cU7+NlDvoSK\nmc1upd6zXdLZwCeBFPh8RHy0ZXwP8BXy+7BvAi6KiPXFuCuAdwMN4E8iYlU3yyxNc9PW9sdhw8/h\n1R/aPf7oV+eTrfsRH3jNX/A78+fwNyvu5c2fu41D5/Zw5nGHcVB/lURCEnN7KszrqzK3t8JwI+Pp\noTq7hhv01yocMqfGggNqHNhXZU5PhTm1lKeH6jz+1CC/3TZEPcuYU6vQ35Myr6/KwgN6OGROjUqa\n0MiCncN1RhpBmohKIiqpqKUJUn5/+YhgqJ4x3MiXk3Zx3/nhesbmHcNIUE0TapX8N0ijEdSzjEYE\njSx/JNq9zkqaUElENU26Wo+ZPfeUFiSSUuBq4ExgAFgtaUVE3Nc02buBLRHxAkkXAx8DLpJ0HHAx\ncDxwBHCzpBcW80y0zHKkNdiyHj53BqTVfNgLX7N7/AGHwvNOhLU/hNMv48IXVnjD+47kjoc2cusD\nT3Dnzx/mifocNsUBjEQ67cUb/YIfrmeIjJSMesvu7ankX+aDIw2aK0n9tZT+WspwPWOkUUeNEZJq\nL/21Cj3VhG07R3hqcPyj0UTGEWxiSfJb6lHh4XgeT3AQ0D48apWEQ/przO9PmVsTTzcSBkcyhusZ\nQV64CKgXQZUFVFNRqyT0VFLm1FLm9FTor1WQ8mlHA3LXSIPBkQY9lYS5vXlYQx6Gw418WYkgkYgY\nXVsulUgSUU1FTyWlp5JQSXcCAflKAAAMwklEQVRvQxZQb2SMNIIsgp5KHqqVJGG4kTE0kq9jtI8s\ngKwI2OZtqKYJEdDIMurF+HoWZFn+A6Cnmo4tu5YmVFORSGQRY/uukuY/FAAGR/LtHq5nRKNBX30L\nvdlOhnsWQM8BVNKUwXqDweEGw42MWpqMrSNNRJoICUbqwXCjUXyORFqso7ea5j9eainDjYyndo3w\n1OAI9Ub+wyFJoCro1TD9GiGSCjvVTyNEEKTS2A+JkUYw0sjIIvLtqyT0jP7oKNbXyMi3tXhfGlnQ\niBj7UVItfqAkiUjE2PtXb+x+PxtZUEkT+qopvdWkWHf+GWtkjH3OUuXb11vNf2ztGKqzY7jBSCOj\np5LQW02ppgkq/s8SiZ5KQk813++7hhv5D8GRxtj/4ug0Y8slfx+yYrsioNHUjxqRl2d0UFpsZ62S\nkI7t96DeKH4E1hvUix9tiUDS2PaPfs4qiahWEl5/4hHM66+O+/87VWXWSE4F1kbEOgBJ1wHnAc1f\n+ucBf1M8vwH4B+U/m88DrouIIeBhSWuL5dHFMstx2h9BtQ82r4PND8ORL4XDTnjmNEefAf/2CfjI\noQD0Af+peJAANQARfQcRSoks/8KRQCj/zo3igwZElqGoo6xBSJD2QKWGlBBZA7I6EUGDlDopiga1\nxk5q2S4AGqoykvZTT2okxXJEg6ilhCqEEhQZRANlDaoaopKOQAoZCUNZP4PDfVQrdWpzhqhkQ4DI\nVCFTSpZUyVQlS6r0Dm8qxu82kvQyVD0IIgMyxr6xJZTVqY7spLY1n2dYNXYlcxhJ+hCRPyIjpUFS\nPKgHquf/cBkJjUiok5KRkCkhI8mXDUgJlRiilg3SE0M0SBhWjWH10iBltDCRT00U/+oJDZJoIIIG\nCfXIlyuypikZy8cIEfm7QpUGNUZIyfLyKS9bvvuDhAxFHvQJwQgVhlVjhCqZ0rHITchIok4adUSQ\nIbLIS9AotjchYw676GeQlIyd9DKoHhLgYLZSYXf/3C562BJz8/AkSJSRRZIvt2kbYnTrlL8TKQ0q\n1ElpkIUYIaUeKRl5rVoSNer0MUgfg/TyzMsEDUfKFg5kJ31jpUnI6GWEmuokZAxHhWGqjERClQZV\n1anQINDYns/3zW6jH6OI3dO0ExKKKPZrPle2+9M1Np2afkqkZPRpiD6GScjYRY3B6GG4+KpMis/B\n6PsFeXj2MkyNer5PqTAc1d0fd0DK939CjG1bFPs0xj5dLeVvKutoGZvLmpBRVYMeRkiL9ywY/Tzu\nLuu2Q77PvGNe1PY9mi5lBski4JGm1wPASztNExF1SduA+cXw21rmXVQ8n2iZAEi6FLgU4Kijjtq7\nLWh2xDI44hPjT/PS9+Y/R2pzoO8Q6J2X960giAbs3Aw7nkQ7n8y/wJXQ7hd7/m8RoDRvUkuS4uf5\nEDSG8udJpVg2+UEAWT1fXs9cqB0ASUo6vIN0+GmoD0JSzWtSSvOyZPV8viTdvZ5KTx6WaZVkZBd9\nQ9vpG3o6n6/aD9VeiCDJ6vn8jeH8UR+GOQtg/gvyR2MYNq+junkd1cGn8vdEYiwpR8vfcwDU5kKS\nUBvaTm1wGwzvzLdDSfHTrtK0raPLIQ+n0XJkWbFNowdDFOuo9EKtP/8bGX0ju/L3onW65jpJUt39\nvkaWTxuN3WVq+5VGPjyt5jXXtLp7n2T1YvuLeUffbwkaI3l56oNF2FK8N2mxvypN71mWjxtdbpLm\n+7k2B5KUecM7mTe8I5927vNg7uH5uB0b6Xv6Cfp2bt5djt1VuGK9Te9D8zCl+fYkefBmjREaI8Mk\nBHklLfIfN7U5+ftcnZN/fqp90BimtuNJDtv5JAzvaHqbknx/VHry5TeGoD5EZHUiyX+UhNIiABp5\n8I7+0GL3r/YsCyJrEE37PW82Lv4W2xLKv07rxdubiqYfBcW/ZlE7aGR5+dLeA6jU+lBSoX9kF9nQ\nDrKRwbHPQJD/2Gs0GvnuqvVR6ekjrfXS16gT9UGiPkSWQSOyYrka2+/5ujMUDTT2vmeMbaXy2nWW\nBVnWyAtYBLeAJMlrkUoSIukhKz6zksbCI//xAVmIAw5duMd3zHQrM0jatWm09jp3mqbT8HY/Pdr2\nZEfENcA1AMuXL392ersPPAL+4G+elVXt+86Y6QLYNEso7+ic0S/2iZbf7XTN06fFY7xpEqBd40+Z\n2zyblPkeDQBHNr1eDGzoNI2kCjAP2DzOvN0s08zMnkVlBslq4BhJSyXVyDvPV7RMswK4pHh+AfCj\nyHspVwAXS+qRtBQ4Brijy2WamdmzqLSmraLP4zJgFXnN8tqIuFfSVcCaiFgBfAH4atGZvpk8GCim\nu568E70O/HFENADaLbOsbTAzs4lpf7iUx/Lly2PNmjUzXQwzs+cUSXdGxPKJpnM/kpmZTYmDxMzM\npsRBYmZmU+IgMTOzKdkvOtslbQR+vZezLwD2x3vo7o/bvT9uM+yf2+1t7s7vRMSEp8bvF0EyFZLW\ndHPUwmyzP273/rjNsH9ut7d5erlpy8zMpsRBYmZmU+Igmdg1M12AGbI/bvf+uM2wf263t3kauY/E\nzMymxDUSMzObEgeJmZlNiYNkHJLOlvSApLWSLp/p8pRB0pGSbpF0v6R7Jf1pMfwQST+Q9GDx9+CZ\nLut0k5RK+rmk/1m8Xirp9mKbv1XcqmBWkXSQpBsk/arY56fP9n0t6f8qPtv3SPqmpN7ZuK8lXSvp\nCUn3NA1ru2+V+1Tx3fYLSS+ZyrodJB1ISoGrgXOA44A3SzpuZktVijrwFxFxLHAa8MfFdl4O/DAi\njgF+WLyebf4UuL/p9ceAjxfbvAV494yUqlyfBP5XRPwe8GLy7Z+1+1rSIuBPgOURcQL57ScuZnbu\n6y8BZ7cM67RvzyG/z9Mx5Lck//RUVuwg6exUYG1ErIuIYeA64LwZLtO0i4jHIuKu4vl28i+WReTb\n+uVisi8D589MCcshaTHwOuDzxWsBrwZuKCaZjdt8IPAK8vsAERHDEbGVWb6vye+71FfchbUfeIxZ\nuK8j4l/I7+vUrNO+PQ/4SuRuAw6SdPjerttB0tki4JGm1wPFsFlL0hLgJOB24LCIeAzysAEOnbmS\nleITwH8BsuL1fGBrRNSL17Nxfz8f2Ah8sWjS+7ykOczifR0RjwJ/B/yGPEC2AXcy+/f1qE77dlq/\n3xwknanNsFl7rLSkA4BvA38WEU/NdHnKJOn1wBMRcWfz4DaTzrb9XQFeAnw6Ik4CdjCLmrHaKfoE\nzgOWAkcAc8ibdVrNtn09kWn9vDtIOhsAjmx6vRjYMENlKZWkKnmIfD0ivlMMfny0qlv8fWKmyleC\nlwHnSlpP3mT5avIaykFF8wfMzv09AAxExO3F6xvIg2U27+s/AB6OiI0RMQJ8B/h9Zv++HtVp307r\n95uDpLPVwDHF0R018g66FTNcpmlX9A18Abg/Iv6+adQK4JLi+SXAd5/tspUlIq6IiMURsYR8v/4o\nIt4K3AJcUEw2q7YZICJ+Czwi6XeLQWcA9zGL9zV5k9ZpkvqLz/roNs/qfd2k075dAbyjOHrrNGDb\naBPY3vCZ7eOQ9FryX6opcG1E/O0MF2naSfrfgJ8Av2R3f8FfkveTXA8cRf7P+KaIaO3Ie86T9Erg\n/RHxeknPJ6+hHAL8HHhbRAzNZPmmm6Rl5AcY1IB1wLvIf1DO2n0t6cPAReRHKP4c+D/I+wNm1b6W\n9E3gleSXi38c+Gvgn2mzb4tQ/Qfyo7x2Au+KiDV7vW4HiZmZTYWbtszMbEocJGZmNiUOEjMzmxIH\niZmZTYmDxMzMpsRBYraPk/TK0SsUm+2LHCRmZjYlDhKzaSLpbZLukHS3pM8W9zt5WtL/J+kuST+U\ntLCYdpmk24p7QdzYdJ+IF0i6WdK/F/McXSz+gKb7iHy9OKHMbJ/gIDGbBpKOJT97+mURsQxoAG8l\nv0jgXRHxEuDH5GcbA3wF+GBEvIj8qgKjw78OXB0RLya/JtToZStOAv6M/N44zye/XpjZPqEy8SRm\n1oUzgJOB1UVloY/8AnkZ8K1imq8B35E0DzgoIn5cDP8y8E+S5gKLIuJGgIgYBCiWd0dEDBSv7waW\nAP9a/maZTcxBYjY9BHw5Iq54xkDpQy3TjXdNovGaq5qvA9XA/7u2D3HTltn0+CFwgaRDYexe2b9D\n/j82epXZtwD/GhHbgC2SXl4Mfzvw4+I+MAOSzi+W0SOp/1ndCrO94F81ZtMgIu6T9FfATZISYAT4\nY/KbRx0v6U7yu/NdVMxyCfCZIihGr8ILeah8VtJVxTLe9Cxuhtle8dV/zUok6emIOGCmy2FWJjdt\nmZnZlLhGYmZmU+IaiZmZTYmDxMzMpsRBYmZmU+IgMTOzKXGQmJnZlPz/ja9TxJG7KMMAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f640b29d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['mean_squared_error'])\n",
    "plt.plot(history.history['val_mean_squared_error'])\n",
    "plt.title('model mean squared error')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The score below is best on kaggle kernels till date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The mean square error for validation set is 0.000328 which is very low and good as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_uuid": "a886a1e531970ccd702207858d59aa98a62b1a14"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.00019 MSE (0.01 RMSE)\n",
      "Test Score: 0.00033 MSE (0.02 RMSE)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.00019119656081728712, 0.0003285944225665714)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def model_score(model, X_train, y_train, X_test, y_test):\n",
    "    trainScore = model.evaluate(X_train, y_train, verbose=0)\n",
    "    print('Train Score: %.5f MSE (%.2f RMSE)' % (trainScore[0], math.sqrt(trainScore[0])))\n",
    "    testScore = model.evaluate(X_test, y_test, verbose=0)\n",
    "    print('Test Score: %.5f MSE (%.2f RMSE)' % (testScore[0], math.sqrt(testScore[0])))\n",
    "    return trainScore[0], testScore[0]\n",
    "\n",
    "model_score(model, trainX, trainY , testX, testY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inverse transforming the scaled vector earlier and checking for the error visually in the graph.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_uuid": "34c8dd72e7adb1a009eb99e65f9e2d2f87322022"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[36.707367],\n",
       "       [35.363888],\n",
       "       [35.62965 ],\n",
       "       [36.36585 ],\n",
       "       [36.40129 ],\n",
       "       [36.104248],\n",
       "       [35.64992 ],\n",
       "       [34.80877 ],\n",
       "       [33.6898  ],\n",
       "       [32.265743]], dtype=float32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(testX)\n",
    "pred = scaler.inverse_transform(pred)\n",
    "pred[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_uuid": "94808f96affd073b0126a47b6c8cfb747bc39b1e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34.49    ],\n",
       "       [35.93    ],\n",
       "       [36.24    ],\n",
       "       [36.1     ],\n",
       "       [35.69    ],\n",
       "       [35.19    ],\n",
       "       [34.1     ],\n",
       "       [32.93    ],\n",
       "       [31.31    ],\n",
       "       [31.739998]], dtype=float32)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testY = testY.reshape(testY.shape[0] , 1)\n",
    "testY = scaler.inverse_transform(testY)\n",
    "testY[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The space between the predicted (red) and actual (blue) line is self explanatory for the model tuning and performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_uuid": "16d755029d8cd6b666d4e547841eb37d03b21e08"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Red - Predicted Stock Prices  ,  Blue - Actual Stock Prices\n"
     ]
    },
    {
     "data": {
      "image/png": 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dswYW0zRzQxAI1g7isKMJBJs3t2I7X38drMhDwZjVq205bVoVNm/Oum3nTmtz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\nxhuShvfii14b7rpLurRPn17hfH74QUIab7sNyVHs27fM2Eg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Wmv79Zbl4sfQX3LkTrFYJ\nWN+uq94kvTJwhODmzbKs4Zc/KeLipCIrIOI+JoaAfem0GYAIQcvyjROuKIriA5R7C9QY851lWf5A\nF2PMRyUfNTjHuo3jCO7dy+TJcvFxQx/57/p7lp2hHxYm3YUnT4YjR2ppooqiKL5PSoosmzZFmvvF\nx1fJsrrqKikoc9VVMHs2jBpVPfOsKu3aSYqiUzDG3UOwTZviJT1rgLrsCDZrJu0jDh+W567YOELz\n0j2OYIsWEBRUq3NUFEXxFSqMhTHGFAGVu9WqlI1XaOgnn0gqy4u3yH/X+95sT1GRPe6mmySJXWKV\nFEVRlDJwhGB8PJLk17FjlfYfPx5eeQWmThWx4CtC0M9P3EpHCO7aVXs9zxs1kuWWLbKMiamdeZwI\nTv9EpyDQkUaxxJCurSMURVHKoDJJESsty/rWsqwbLMsa4zyqfWanC3ZoaPqavaxYATfcAIE7NlMU\nGMTKAy1ZvtweN2iQlE6bM6fWpqooiuLrpKbKsmlTpJrJCVSA/POfpcn8mDEwfPipnd/J0K8frFsn\nPQ/37as9IRgYKLmUx45Jr8S61HvdaXO4e7cs9wfFEkea6L9t27RiqKIoiheVEYIRQBYwHLjYflxU\nnZM6rQgPx+Xvz8bfMgkIkEa/bNqEadMWY/l7DMCgIBgwAObOrc3ZKoqi+DRuRzB4v8QAnuCF/eWX\nw9dfS+sGX+GCC0SE3X+/PK/BjhGlcFzBupQfCKUdwQxiiWYv7Y+skioy6ggqiqK4qahYjMMEY8y+\nap/J6YplURAeTsa6vZx/vh0punkzAV270quBRII+8YQ9duhQ6S+4f780Q1IURVGKkZoqxSCDdp9+\nFSD795dG9VlZkJsLiYm1N5ewMEhPr1v5gWA7xXiE4M5jcfSmiMhLzxZVe9lltTc5RVEUH6Oi9hEX\nW5aVCayxLGuPZVkDa3BepxXHwsMJycukTx+goAB27ID27Rk5UvJBcnLsgUOHSm30efNqcbaKoii+\nS0qKV1gonHahfv7+csOwdWspcFlbOAVj6poQDAmRcFYnNHRLrrwBq00bWLKkdtW1oiiKj1FRaOiz\nwDnGmHjgcuCfNTOl04/DoRFEs5fISGD7digshA4dGDVKqojOmmUP7NsXgoM1T1BRFKUcUlLsQjFb\nt2orgGqkrgpBkDxBxxGckjeSd7u+CvPne+JGFUVRFKBiIVhojNkEYIxZDDSsmSmdfuSFRBBFpghB\npzFT+/YMGCBVwYvlCQ4cqHmCiqIo5ZCaajuCTiuA4ODantJpiSME61qOIIgQ3L0bXC5YlxzKppH3\nQkO9hFEURSlJRUIw2rKsvziPMp4rlSSnXhOPI+glBAMDpWLdjBkSEQrAkCGwahUcOFBb01UURal9\n0tLgq6+8/jhKMEVGhpcjeJqFhfoSddkRbN5cHMHUVMjPP63SSBVFUU4pFQnBdxAX0HmUfK5UkgMB\nkTQil6iG+dKhNzbW/V925EhIToakJHuw5gnWbR5+WEr9RUZKx+o6wttvS2EIRfEJpk6Frl3hyis9\njfUQEehyeeUI6hV+tVGXhaDTVH7tWnneunXtzkdRFMVXKbdqqDHm6ZqcyOnMPj/pJRjNXli6tFgD\n5EGDZLlggZ3D3rev5L0sXw6XXFILs1WqyrFj0KMHvDA+iYtefBH69JGY36lTpQSgL9WnL4OMDGmy\nffAgTJhQ27NRznjeeQfGjZNfquxsmD5dWuvgaR2R0Gi/RE2oI1ht1GUh6KQCOlkWKgQVRVHKpjJ9\nBJWTJBMRgk3mTZNuwWPHurd16SKaYeFCe0VwsPwX27GjFmaqnAhr18KGDZD98nsi4r/6Cp5+WuLY\nFiyo7ekdl+xsWe7dW7vzUBRAfn86dBAncOBAEYI2jhBseez0rBjqS3TuLNVLnQbtdQlnznPnQkCA\npJIqiqIopVEhWAOkFcUAEPDicxAeDtdf797m7y+9o4rphcREFYJ1iOXLwZ9Chu98n6PnXiBC/uyz\n5eTWgcI/TvsSFYJKbVJYCEUFLinxf845UK8eXHih5EzbCjA1VcbG5dlCUENDq43LL5dogZCQ2p5J\n1XGE4NKlkJAgYlBRFEUpzXGFoGVZQWWs027nVWDPMbvsWkYG3HJLqVDBgQNhzRppIAxIHMv27TU7\nSeWEWb4cLvabTjxpzG03TlY2bAg9e8Jvv9Xu5CqBCkHFF7jyShgStwWysyns3V9WXnihLH/4ARA9\nGBAAjfZuE/dde8IpZeAIwaIiDQtVFEWpiMo4glMsywp0nliWFQf8Un1TOv3YnW8LQcuCu+8utX3g\nQCmAsGSJvSIxUUTjoUM1N0nlhFm+HP7a+B3S/eN5detoz4YhQ2DxYjhypPgOe/cWq4RY2zhCMCOj\nduehnLns2gXffAM9CxYDcOObthDs3Fni+uzw0NRUyVmztm2V9UGl7lMqCiEh0KSJ/KymsaIoSvlU\nRghOA760LMvfsqwEYAbwSHVO6nQj5WAER/wbyN3tMu5g9+snGtEdHurcwtTwUJ/n2DHIXZNE3/0/\nsq73zfwyO8Cdc8eQITJg8WLPDrNmyZXs11/XynzLQh1Bpbb5+GO5N/LMRYvID2rE56s7SBVby5K/\nm7/+CkePeprJb9ig+YFKhTgFY9QRVBRFKZ/jCkFjzDuIAzgN+A4Yb4z5ubondjqRc7AeE8/9Tmr0\nl0F4uNz4dgtBRyyqEPR51q2DcQWvg58fjR8ZT0EBfP+9vXHQILmQdcJDs7PhppvE/p09u7amXApv\nIehDRqVyhmAMfPghDBsGjTYu5nCXvhj8WL3aHnDRRRIdMX06KSkwOGSp5A2ef35tTlvxcZzwUBWC\niqIo5VOuECzRPD4YaA6sAvprQ/mqkZMTyL4uw+xb2WUzcKBUDnW5UCFYh1jzey638h6HLriSHhc3\nIy7OSwiGh0O3biL6Dh2Ce++V2LaEBK844NrHEYIFBZ6fFaWmmD9fUqJvveYwrFlDyFAJC3ULwfPO\ng/bt4ZFH2JtSwLW7X5DeBuPG1d6kFZ9HHUFFUZTjU5Ej6N08PhSYCmxDG8pXiSNHID/fn8jIiscN\nHCgX4Rs3AhERcqGjBWN8ki1b4M47pbhP8BcfEU4OoY/dh5+fFAtdutRr8NChMGeO9Aj55BN49FG4\n+mq5ys3Pr6V3UBxv8bdvW7Y0FFSUGuKDD6S20piWy6GoiJBh/WneXEw/AAID4eWXYcsW/nbwz3Tb\nMQXuukt2UpRy6NwZGjXSekKKoigVoQ3lq5msLFkeTwh26ybLTZugc2dLW0j4MNOmwX//CxlpLiYu\nfZX1jfrTuX8/AHr3ljZoWVl2sYKHH4bERA7vP8Ke3HDaPXGzFL4oKICVK92NsmsTbyEYd+XZkLJV\nBOx993mqNipKNfHtt3DppRCyepGs6NuX7t29HEGA0aPZ1WEk92x6A1dgENZ999XKXJW6wx13SCXa\nutj+QlEUpaaoTPuIXyzLCvd63tiyrBmV2C/YsqwllmWttixrvWVZT9vrP7QsK8myrFX2o3s5+//R\nsqyt9uOPVXlTvsS+fbI8nhBs2lSWTsNkbSHhuyQlyTLlm6W0OLqNNYM8lWD79JHlsmWyPFAvhns2\n/4nolx6i/cvjWL0hEPr2lY3eRWRqkZwc8PMDP4oI2bVZbqVv3QpjxkgSpIMx8oXevFmTCZVTQl6e\nP1lZ0LUr8vuQmAhRUXTrJl8zd8Fdy+Lv4a9QiD9+t9wMMTG1OW2lDhAQANHRtT0LRVEU36YyVUOj\njDFOHUSMMQeAyvx5PQoMN8Z0A7oD51uWZdcEZ4Ixprv9WFVyR7tP4VNAP6Av8JRlWY0r8Zo+R2WF\nYJMmEgHlNEwmMRGSk6URkuJTJCdLi8BbBklT67Dhvdzbetk/OuGhTzwh7uE558jzrVuRXNFmzXwm\nTzAnB1q2hCgy8XMVwW23yUV5eDjccIOEsD7/vMRZRUVBhw4waVJtT1s5DUhPDwagVSskFtT+Bere\nXf70rV8v43Jy4KNlnfnPzaskTFRRFEVRlJOmMkKwyLKsFs4Ty7JaAse1A4yQZz8NtB+VtRFGAb8Y\nY/bbwvMXoE6WiMvMlOXxhKCfn+gDtyOYmCitB9wrFF8hKUkuXG8ZLtbg8FsS3NvCwqBdO3EEXS6Y\nMgX+8AeYPFm279xpD+zXz6ccwTZtII40WREfL7fSJ02Si/O2beGRR2D4cJg4Ueb+xBOaS6icNOnp\nEreXGJ0nERBnnQV4QuWd8NCffoLCQuh3axeoX782pqooiqIopx3l5gh68Rgw37KsufbzwUClyrVZ\nluUPLAfaAG8YYxZblnUn8KxlWU8CM4GHjTFHS+zaFNjt9XyPva6s1xjnzCcmJoY5c+ZUZmo1xsKF\nTYG2bNnyOxkZBRWODQ3twfr1LubMWU3jvDy6AaumTCG7e5nRs0o1kpeXV+Z3yRhISjqHbt1S2bdk\nARERESxaXVzQtWjRkd9/D+fNN9eTltaTjh03sGrVXho0GMTvv6fTq9c2mkdG0nrHDn6fNo2C8PBS\nr1OTZGT0JSwslzYhu+AILE9LY+eUBRQWRjPowguJ+fVXtj74IOmjR4Nl0TA4mF7jx7PzzjtJuv32\nWp27N+WdM8V32bkzCoC8xZ8BsNayyJozB5cLQkIGMX16Oq1bb+OddzoSHt6Y/PwF6CmuXfT3rO6h\n56zuoees7lFnz5kx5rgPIBK4yH5EVmafEvuHA7OBLkAcYAFBwEfAk2WMnwA87vX8CeCB471Or169\njK/x5JPGWJbLFBYef+wVVxjTvr39ZPt2Y8CYd9+t1vkpZTN79uwy16elyWl57TVjzNChxgwYUGrM\nxIky5uqrjalXz5icHFl/1lnGXHyxPWjOHBn0/ffVMv+qEB1tzB13GPNE7CSZ065dZtQoY5o3Nyb/\niMuY3FxjjDH793vtdN11xgQFGbNpU+1MugzKO2eK7zJmzG4TGmqM6237u7djh3vbwIHGnHOOMfn5\nxoSHG3PzzbU4UcWN/p7VPfSc1T30nNU9fO2cActMJTRaZUJDAQYCQ+1H/wpHli02s4E5wPnGmDR7\njkeBD5AcwJLsQfoWOjQDUssY5/Ps2wcNGxbi73/8sU2beuUItmgB/v5aOdTHcArFtGqFJ0a0BL17\ny/Lzz6UFWqNG8rxlS6/Q0F69JB7YB/IEc3JkjglB8uUzMbEsXgy7d8NHH1sQGsprr0m0qPvr+Nxz\n8v3s2FF6n/z8c+29AaXOkp4eTKtWYK1dI+0gWrZ0b+vWDZYvl69YdjaMHVuLE1UURVGU05DKVA19\nHrgP2GA/7rMs65+V2C/KqTZqWVYIMALYZFlWnL3OAi4F1pWx+wxgpF2htDEw0l5X59i3D8LCKg4J\ndYiPl950ublIybMWLTzKQ/EJkpNlmdC0QJRSGU2qevTALfzHjPGsT0jw7E9oqOTerV1bjbM9PkeP\nyiMsDJr7p7LPP5oduwPJzpav4HPPwYYN8Ne/So7WL7/YO7ZoIQlcTz8tFXBeeKFW34dSN0lPDyYh\nAfk96NJFbo7Y9OsHhw/Ld/PHH2HUqFqbpqIoiqKcllTGERwNnGeMed8Y8z5StKUyzcXigNmWZa0B\nliLFX74HPrMsay2wFgk5fQbAsqzelmW9C2CM2Q/8w95vKfB3e12doypC0Gkh4XYFi1mEii/gFoL+\nu6UaTBmOYP360oHB318KxTi0bCn1VbKdGrxduhRvz1ALOD0Ew8Ig1pVKKvEsXy7rnnxSHMzBgyEo\nSAqGFgt/b9NGisace66X1akolcMYSEsLplWCgTVr3IViHK6/Xgzz5cvh/DpZKkxRFEVRfJvKFIsB\nyfFzhFhYZXYwxqwBepSxfng545cBt3k9fx94v5Lz81mq6giCFApt395esapUdw2lFklKEkHUYK93\njGhpxo0T0dikiWddQoIsk5OlPD5dusDUqdIsrZa6HnsLwSZHU1lVFMfixdLK5KGHYNo0WLECPvoI\nZsyAmTPlAt6yvA7SsqW8D5ermKOjKBWxfz8cORJAl8Z74MABu5mgB39/T19ORVEURVFOPZW5avsn\nsNJuBP8RUgX0uKGhipCZWXVH0N0xIj5eHUEfIznZFnRJFQvBu++Gl14qvs5Jf3KbZ507i3jatKka\nZlo5vIVg2CFxBH/6Sa7Jg4JEAL76qrQTHDoUMjKk0ffRo2IETp2KvLFjxyA9/eQnlJEhyWEffHDy\nx1J8GudXqLPLDo8u4QgqiqIoilK9HFcIGmMmIwViptiPAfY65TgYc2KOoFv7xcdDXp6dNKj4AsnJ\nXoVi/P2lMXwl8XYEAXEEoVbDQ91CsEEh9fP2kko8Gza4+3rTpQvce684gMOGybrZs+HDD2HWLPjt\nN8pQuCfBvHkSJnjLLfDPf8ovkXJa4g6zPrhGfijhCCqKoiiKUr1UpljMTLvS57fGmG+MMemWZc2s\nicnVdXJzoaCg8kIwNFSqNxZzBEFdQR/B5RKt43YEW7SQiiqVpEkTyR9066U2bSQGc/366phupXCE\nYJOivVguF6nId84Rgt60bi2u9c8/SxEZkIg+WrSQJ6dCCK5ZI+GlV10Fjz6qzuBpjOMIRqaugebN\noZb7aSqKoijKmUa5QtCyrGDLsiKASLt6Z4T9SAD7alGpkH37ZBkWdqzS+xSrDxMXJ0sVgj5BWppE\nQLqFYDlhoeVhWSUqhwYGQocOPuEINj4i3zFHCPbsWXqs4wpOmwa7dkG9erYQdBzBXbtOeB6HDsFX\nX4FZs0YSZCdPhnbt4OuvT/iYim+TnAyhoQXU27xWw0IVRVEUpRaoyBG8A8kH7GAvncc3wBvVP7W6\nT0yMFNjo1etApfeJj1dH0FdxBFxFPQSPR7FeglDrlUMdIdgozyMEAwLKj9IbOlSWvXvDgAF2BdRG\njcTNOQlH8Nln4cor4eji1SIK/PxgxAiJPS2onKOu1C2SkqBN9F5xxMu686AoiqIoSrVSrhA0xrxq\njGkFPGiMSTTGtLIf3Ywxr9fgHOssDRrAyJEQFXWCjqAKQZ/CXR8m5rAUNTlBIeh2BEEKxuzcWWt5\noI4QrJ8t37F9gfF06QLBwWWPP/98ucHx3HPQuLHtCEIZCrfyHD4MkyZBI3IITk/2uEPDh0uO7NKl\nJ3RcxbdJTobzQuZKzLVzh0FRFEVRlBqjotDQPpZlxRpjXrOf32hZ1jeWZf3HDhlVqgGnUKjLBTRs\nKGoyLa22p6XgEXAtjf3DCQjBhAQpm+/WfU7BmA0bTnJ2J0ZOjnzF/DNSwc+PiA7RFV6TN20qxUHP\nO+/UCcHPPoOsLBgVL86oq2s32TB0qMSjztSU5NMNY+T3aVDBbxJjPGBAbU9JURRFUc44KgoNfRs4\nBmBZ1mDgeeBjIAeYVP1TOzNp2hQKC6XtBJalLSR8iORkiI1FXCs4YUcQSrSQgForGHPwoER2kpoK\nMTH8tiCAF16o3L7h4WUIwSpW+TRG2lN06wYTRkn1yKVHbUewSRNpuDhrVpWOqfg+W7dK+8zu2b9D\n//611kdTURRFUc5kKhKC/sYYp4n8WGCSMeZrY8wTQJvqn9qZSZktJFQI+gRJSXahmK1bZUViYpWP\nUaqFRKtWchG8fLltA9csOTnSQ5C0NIiLIzRUDJrK0LixFHkpKECEYG6unTRYeWbNEg18//3QzVrN\nAcL5bK5XS45zz4UFCyR+VPEt1qyR6kEHD1Z510mToLFfDs0zN2hYqKIoiqLUEhUKQcuynNr45wLe\nt+UrXzNfqRLaVN53cfcQXLsWoqIgOrrKx2jbVpZuA9DfX3Li3nxTektcc02N9s5zC8HUVM9diErS\nuLEsi1UOrWJ46Hffydu++mqot3ENqU3O4quvLY8mHj5cSrV+/TXcfjuMH1+l4yvVyKefwpw5ItSr\nwOHD8P77MGHAfCyj+YGKoiiKUltUJAQnA3Mty/oGOALMA7Asqw0SHqpUA+UKQW2sXasUFUl3hIQE\nYPVqiWW0rCofp0kTaR+4aJHXys8+g//+Fy6/HD7/HH799VRN+7icCiGYnc0JC8G0NGjWDILruWDt\nWgJ6dyMtDRYutAecc470arzxRnj3XXj7bWk6r9Q+TsjuihVV2u3zz+XmwfVNZ+MKDNT8QEVRFEWp\nJSqqGvos8ADwITDIGLcS8QP+VP1TOzOJiRF94a4PEx8vyTQ5qr1rk5QUyd1s1aJI2j2cRN+z/v1F\nCLp/o1q3hjvuEJskPp5KJ+mdAnJyIKJhAezde2ocwSr2EkxPl7xLkpIgL49mF8jnOneuPSA0lF2j\nbmP/8MtFgEdHwz/+UaXXUKqBAwc8ArAKQtAYeP11SY1ttn0OBzt1Kr9EraIoiqIo1UpFjiDGmEXG\nmKnGmENe67YYY6p2C1ipNIGBEBlZQgiCVg6tZZycvk71tkF+/kkLwfR02L1bnv/wg22uBAVJstzM\nmZIzWAPk5ED/PNuBdCqYVpJiQjAqSi7oq+gIuoXgGikU06B/V9q39zimLhf0XPQWV1lfyWc+YQL8\n8ksJS1WpacycuaLqWrSo3HfVGDh0iJUrYeVK+PMf92OtXEl29+7VP1lFURRFUcqkQiGo1A5xcXKB\n7H4CmidYyzg9BBPzRLCcjBDs10+WixZJ+tsNN8CDD9obx42TMp4vvnjik60COTkwYutb4rRdfHGV\n9g0Pl+WBA4iN3aLFCQnBmBhg40ZZ0akTAwZIaKgxko6ZleWVUzl+vNwpUVewVigokFTNz8fNkuTO\n226TuyT791e848SJEB/PhrmZAFxSNAVcLvZpWKiiKIqi1BoqBH2QuLgyHEEVgrVKcrJonai0NVLg\npWPHEz7WWWeJebZoEfz4o1xDr19vV98MCxOx89VXsGfPKZt/WRQUQOSRXXRMmg633lr5cqE2xRxB\nqHIvwSNHpOBkbCywebMkyDZsyIABsG8fbN/uSUNLTxdBSGiouKY//ADbtlVpvsrJkZsLF14oqZpd\n982icMA5Ym+D2HzlkZ8v4c4HDxI+7UOCgiByxv+gbVvy2rWrmckriqIoilIKFYI+SGyslxBUR9An\nSE4WTR6wfjW0b39SeU316kGvXrB4MXz8saw7dky0ECAui8sF//d/Jz3v8nDSTm/nHSyMOJFVpFix\nGJBKOu6+GMcnI0OWbiHYvj3g0RaLFokQdGryuF3BG2+U5eefV3nOyonzpz/J+bhxVAZdWE9W9+HQ\ns6dsLJEnWFgoDwA++UROdmwsPZdPYmDLFKy5c6RC7gkUXFIURVEU5dSgQtAHcUJDXS7EAWnUSIVg\nLePuIbhmzUmFhTr06yepVd9/DyNGyLrVq+2NbduKUpw8+aRfpyw+/RQiIuCDSQXcxrvsOWu0p8Fh\nFQgKkhaIbkcwMREyMyEvD4CjR6UwSMFRFzz7LHz4YbE+g074c2yMgU2b3EKwc2do2FCKg86dCxdc\nIOPcQrB5c6km+r//aTXdGmTlSjkXj/SfDUByq+FSBrdly1JC8IILpCUILhf861/yff7Xv4g/tI0X\nD46X83bNNbXwLhRFURRFcVAh6IPExcnddHfajfYSPPUcPAhvvQWjRsHAgWLJVUByMnSMz5HQx27d\nTvrl+/cXoXTsmGikevVg1SqvAddcI0rRaV5/ClmxQqL1Zj32K3GkkzK66m6gQ+PGJYQguBMqf/hB\nXKR5kzbC44/DzTdLQqBtgzpCsGngXrEnO3QAJPK2b1/Rebm5YgA2auQlBEE+n40b3UVmlOpn925J\nA43fPo+DNGRDUA/Z0LNnKSG4cqW0ftz2729hyxZ46CEKL72CfTShd/r30KOH+3wriqIoilI7qBD0\nQWJjZVksT7AMIXjkCFxxRQmtcPiwKAylYu66Sx5r1khlktmzyx1aWCjpen1D1sqKU+AIOuGPnTtD\nnz5SsNM7QlhsAAAgAElEQVTtCAKMHSthc9XgCiYlSceK8W1ncpR6cN55J3ysYkKwVStZ7tgBiMkH\nkDXLfmMffihu57//DXhCQ+Nz7ZhY2xEEaS1nG4sMGyaf07p1Xi98xRWiGKvJNVWKk5cn57lFCwjd\nspwV9GR3qr9s7NlTxN7Bg4D8CcrKsvd74U1xm8eMISk1iA+5STaoG6goiqIotY4KQR/ESQt0C8GY\nGOnzVoJ16+Su+xdf2CtSU0VR3HBDjcyzzmIMzJgB110nqig0VD7Icti9WxrKdy46+YqhDs2awWWX\nwSOPiN7r1k0cQXekY7NmEv44efIpD39MShIz5pIGM8nvOZC+Q0JO+Fjh4WU4grYQ3LJFnppVq6Uv\nyjXXiL23ahXs2eN2BBtnli0EQb7O0dEiBIs5glFRImA//1zDQ2sAp9VJi/hC/NasZlNIT08tIydP\n8D//gSNH3OvPaZtO18yZpI+4HgIC2LwZXuU+soaOgT/+scbfg6IoiqIoxVEh6IM4QtDdQiIyUnKv\nSuAIxWXLkKvxUaPkKr+GetDVWTZtkrKUw4ZJ0ZcLL4Rp00TtlYFT/6RlzhpRPk2bnvQULAumTBEt\nCtC9u5xi9zkHEU6bNpWwwqrAmjXFcvJANFNSEnSOzcJatYqwy87F7yT+ChRzBCMiJIbTDg11it9E\npKzBdOok8a8XXSQrp08nPV2+2gFbN8l5aNHCfdx+/eQzGj5cnnfuLKes2P2QsWMlVLeYlapUB7t2\nybK9ayPk55MS4yUEhwyRmxZPPAEtWnBo6s8AvDb4S/xx8fxOcf+2bIE9NMf6+mtR94qiKIqi1Coq\nBH2QUqGhUVGSQ1VQUGycs33pUuCqq+RK69xzRbloeGj5zJsHQH7fwfL88stFhc2fX+ZwRwhGZG6C\nTp2qpdKhk3ZYLE/QqSKzdGnVD5iVJTGn554rMcQ2+/dLBN+gAjsU1lFaJ0jjxl5a07LEFbQdwc2b\npdVcp4LV5LW232DHjhJC+v33nmbymzdDu3Z4K9ImTeDnn0VbgAhBKOEKdukiyyr2LlSqjuMItsyS\nXMADib08QrBBA5a8NBfX7LkQFUW7F26hAXm0XzGZ1KizeH1WJzIz5TQ3aSL3CxRFURRFqX1UCPog\noaHycAvByEhZOok3No57lJVyBH79FSZMgJtukkp92mPNjTHSmu+77+R54Zx5ZAXGMOTWNrLiggvE\nkSonPDQpSTRKcPKmaitw4QjBYuZWq1Yyr2Lqp5JMnSqVaFasgDvvdIdP2mYdnffOki9Znz4nNe9i\njqAz5x07yMoS0XnjBZnEk8b2BvYbtCxpXP/rr2SnHpZm8l6tI7wZMcLz1Xc0XzFztFQMtVJd7Nol\nvwONk1ZAgwZY7du5heCqVdCvv8XUrMHw3ns0OJDCO9xO8MqF+F17DUVFEr5ezmlWFEVRFKWWUCHo\noxRrKu9cDZcID3W2N8O+Imvf3nOl5W5Kp2Rnw9tvS07e//4H+7+Zx6yCc1ix0pJioaGhElY7ZYrd\ns6M4ycnQKf4AVkZGtQnB8HCpwl/MEfT3l9fbsKHqB/ziC2jTBp56Cj76SJqwr1lD0g4RhPEbZ8Lg\nwZK7dxI0bixmtTuqNjERkpLYvEle5/quomyX5HvlVV50EeTn03rnLJpFHRV1ehyFEBsrr1VME8fE\niLBUIVjt7NolNav8Vi6H7t1p2sKfAwfg0CHphwn2d3fAAOa3u4VrkB6PsfdfTdeu8nu3ZYsYv4qi\nKIqi+AYqBH0Up5cgIKGhIElSXqSlSfXHlpYdt9WsmQrBMnA+toYN4eHrdhF9eCfpbc6hsNBT0ITL\nLoOUlFLum8slkZlDou0SmNVY8r57d7moLhYBXKpKSsUYA9++l4mZNUvChZ98UooHvfYadOvG8Hs6\n8WdeIXinHUZ8kjhN5XNy7BWJiZCfz64l8uVtf1QK7PyQ4tVyY/BgTGgog/dNoXPwdlGRx/lcLUs+\nihUrvGrDBATI74YKwWpn925o2axI1F7PnjRrJuv37JFWEeC5X/Fa0+c56B8ubVkSErj2WliwQE6T\nOoKKoiiK4juoEPRRYmPLcATLEYL9m9pCsHlzKdYRG6tC0AvHSH3vPXiov+QHjnxG8gPdGsu5QnUn\nPgnffCP1Wq7rZQvBjh2rbZ7XXy/uo5MXB0hO4u7d7tL8x+Obb2D6bVOwioqkmIqfn/TtS02Ft9/m\nsCuIV3hABp9kfiCIkwmlK4dmr9hBYCBE7F5Ndv04Zq2N8pitQUEUjLmaG4s+YOy8e2RdJRTCxReL\nKL/vPi8xWMw6V6qLXbugb+MtYgGWEIJOC0FHCK7LiOKJofPhs8+A4p0iVAgqiqIoiu+gQtBHqUxo\naHq6jOsVLULQNLWvztq3VyHohfOxtWwJ93SbB40a0fKirvj5eQlBp4qhV1lKY+Dvf5fWd/3CNknV\ny4SEapvnFVfAuHHwwgswfbq90qmSsnFj2TsVFXka7gH/+heM5f/Y3aA9dO3qGRcbC+PGcVvPldzf\n5nt46SVPYuJJ4DiCJYXg0Y07aN0a/NZKoZjcXC/3FUh+6E3e5VZabLeL1lQiZnDCBPjLX8Tc/NOf\n7JUqBKsdY+ReRG/LVny9ermFYFKSFKcNCJB+pseO2RVGO3d2/660bAmDBsl4DQ1VFEVRFN9BhaCP\nEhcn1/d5eUipPSjmCBqDu+pi+/q72UsUOzOCZaMjBLW/GuARglFRSIzawIEEN/CnTRuv4iMxMbJ0\nupwD338vkXCPPQZ+WzaJIgwIqNa5Tpwo+uzGG+1in506yYaSeYKbN0sFnPh4abuwcycLF0LG71sZ\nwlw+yb+KI/mlq5smJVuk9rgQHnzwlFQ/dYSgu3Joy5ZgWQTs2kHntsdgwwaC+0h+4MKFnv0y9gdy\nO++w6c6JcPfd4mQfB8sSoXvrrfDGG5CbiwrBGiAzE/yPHqJ71kwpXtSxo1sI/vKLFCg+/3y5J7F0\nqfzNat68+DHuu0+0YZs2NT9/RVEURVHKRoWgj1Ksl2BgoMTgeQnBrCzJJYuLg6au3eymuafLQPv2\nYtGUCCU9U3GEYGR4oQgouyF8ly5ejmCDBtLrwEsIPvecGFzXXos4ctWYH+gQEgJ//atU3ExKQiYQ\nFFQ8T9DlgksvhU8+kR5uhYXwxz/y2otH+NL/aopCw3izaBy//SbDp0+HuXPlQn3nTk/f91NBKUcw\nKAjTtCmNspI4J3wtFBQQMawbiYkSoeog+a8WBXfeB6+/XunXsyzo319+zs5GfgEyMsrtAamcPP43\nXksOYXRa/IE0eAwIIDhYAhV+/FHGXH+9LGfMkGVJIXjFFXLTJSio5uatKIqiKErFqBD0UUr1EizR\nVN4pJBMXBw1zdpNqNXPn6mjBmOJkZorGq783WWLXbEHXubN02cjPtwfGxLhDQ42BZcvkAjbQdVR6\n41VjfqA33vlXZVYOdRIX339fqoO++irMnctj03rTvWgF5v0PyQpuxo8/ipgcM0ZaJW7eLG+/VatT\nN9dSQhDIj0+kg2s9180dBw0b4jd8KLffDnPmyLTB8/11vudVISxMlm4hWFSkNz2qi337aDJjMt9z\nEdsmfg/ffuve1KyZuLKhoVII1rI8QrBFi1qar6IoiqIolUaFoI9SqkVaVFSxi11nfVwc+KXsIbtR\nc3ePOLcQ9E7KOpMwRoqj2GRm2mGhjjC2P5/OncVcc+vl6Gi3I3j4sD+FhXbq4PbKVbY8VRQTgs5E\nHUfQGHj+eakSdPnlsu6mm9jW5Q90ZgMHb72foCsvYehQ+OknePhhuUDPypIOElD9QjArLJE+LKPJ\n7pVSMCQ+nptvFmN70iQZk54uGteJeq4KToGanBy0l2B1M0+KK73EBMKvu7BYCK/zPe3eXQz1Vq1w\nRyWUdAQVRVEURfE9VAj6KKWubyMjyxaCDfMgO5ujUc1JTrY3JiTIVfeZ6ghOmiRXor//DsjHVp4Q\nBK+oSy9HMCdH+us1aYLHxqohIRgfL8uUFHtFp05SgSM3V2y1JUswD06gwNj5ipbF0y0/4JEmk2j0\n5gsAXHCBvN0vvhAxeOmlks8FpzY0NCREvmreQnCnf2sADj38jJT6RD7ayy6DDz+U3MeMDBHZfifw\nF6iUIwgqBKuLuXM5FhDC2qA+pUS7IwR79pRlp05yn8Lf33NaFEVRFEXxXVQI+igREVKXxN1LsERo\nqFsIFtoVQ5t5OYIBAVKV4UwUgi4XvPyyLMeNg2PHPI7gpk3yOdpXtO3ayUflLhjj5QgePOglBJ2K\nnTVU+z4oSKZSzBEE0l/5DB54AFd0DEPe/yOjRslmY2DGksakXni7VDZFineAiMoJE+Dpp+W5ZZ3a\nsD3LElfQWwi+Y27joagPCH32kWJjx4+XceedB1OmnFhYKKgjWKPMmcPmiAHEtaxXqraQIwR79JCl\nU9eoaVMRg4qiKIqi+DYqBH0UP78SvQSd0FC7Emh6uuTmNNgvQjC4bXP27oXDh+3xZ2oLiRkzpI79\njTdKXt1LLxUPDfUSc/XqiRgs5ghmZoLL5RaCkZGIgGzeXD7wGqJZM48QzIgUIRj7tzs5tnkHf494\nlXlLg5k9W1IXt26VaTsl+kEKnN51F7z7roTtnXWW9JXv0sWtFU8ZjRtLcRuQCNppi2LJvvSmUlVJ\nhw6VR0aGLP/2txN7PUcIqiNYfezdC3dcdQDX6jXMLBxaZqhnazF+6dtXlo4Q1LBQRVEURakbVG8t\nfOWkaNLEc4FNZKTUaT90CEJDSUuzr4F3ixBs1Eluz+/cadc0ad9eykUWFlZ7ywNf4dtvoc09r9Ip\nLg7eeUdiEP/xDxpwDZGRiTBjM1x4YbF9Onf2NMQmOlqUzP79xUNDt20TZVWDNGuGO9T3/9u77/gq\ny/v/468rgyGEhBlGIAEMyBIUREbBXdFWq9bZVv1ZrVZttdXW2t3a3W+rHXa57bBVqbTWqpUqOKqy\nFCEMAdk77IQRCLl+f3zuO+eckIQEchbn/Xw88rjPuc+6Dve5w3nnc42FVf15ks+zrUNffr7rBna/\nn8ePf2xdPqdMiQSjCRMij3fOlliI9vDDNllMSxs2zIaSVVfbchs7d8IZZxx6P+dg2rSjf72wa+jO\nndhyBgUFCoItaMMGOOssGLzsdbLwPLPtNIbU8/G/9FLrhR4GQAVBERGR9KKKYArr1KlOEITa7qEb\nNgRd69asAefodlIvIBIeOOEEW1+itr/ose/P31jM4DX/Yfe1t1jZ69578fv3c2nVnylqv8NKUXXG\n+Q0aZFW1/fuJrCW4eTM7d1p47twZm3gm7AeXINEVwSXLsridX/GpOV/ktq/l8de/2hITI0bA3/8O\nb7xh7Txcz9XcXKsOtrSrrrJ/2ldesR+oPwi2lNatLf/Vrl2otQRbzI4dtiLJmjXwi4tfxbduzf9N\nH80Pf3jofXNyIkt5gJ1LWVm168iLiIhIilMQTGExQbBrV9sGE8Zs3BhVESwspLjU+vvVBsEMW0Ji\n1SqYMP83VNGK/w29yXYWFVE1cjyX8Az9q2Mnigl162a9bXfsCK4AbNrErl25ZGVBQYcaCxnhDC4J\nUlRkx37PHpv8tW1bm+TlBz+AK66w+3z847ZI+/PPW7fQFlgf/oicf75V6Z54woLg4MFHPv6vqfLz\ng4ogKAi2oFdfta7GTzwBRR+8ihszhlNPa1M7O2xj2re3z+Ltt8e/nSIiInL0FARTWKdOUZNwhBXB\nIAjGdA3t3Zvu3a1ScsgSEhkSBP/xxB6u5k88zWW8tqhr7f7yCZcwgvcYuOzftqNOEIxZ/iCmIphL\nx46QvWOrVVYTHAR7WYGXdessCA4YcOgMm+HqEZs3x3YLTbQ2bawtzzxj1ckzz4z/axYUqCIYD2EV\nevSgCnj3XSsPNsO558b/jwAiIiLSMhQEU1hYEfSemK6hu3fbSgI9emDf3Hr3JisLioujKoKdOtlj\nMiQI7njwaQrYyfTSG5kxI7J/2YmXAND/hfutL1udtRNigmCdimBtt1BISkUQLAi+/74FwboGDYr0\ndI2eKCYZPvEJ+0zu3p3EIBhMpCRHbs0a60LcdfsSm3l3+PBkN0lERETiJG5B0DnXxjk30zn3nnNu\ngXPuu8H+vzjn3nfOlTnnHnHO5Tbw+IPOubnBz7Pxamcq69TJxq7t2UNM19Cw+NG90Ns3tyA1lJRE\nBUHImJlDV62Cs1c8wNauA8k9cwIzZ9p3WIA1WcXMZiQ5FdttmsPc2I9bTBDs1Mnmvd+8OWWC4IoV\nNoaxviAIcO21VsgMp/BPltNPtzzmXLOLSEfkkK6hVVXkVFbG/4WPcWvWWDU6a9kS29HQB09ERETS\nXjwrglXAmd774cAIYJJzbgzwF+AEYBjQFrihgcfv9d6PCH4ujGM7U1anTrbdtg3o0MFCTHl57dqC\nvTvshMrK2mn6GguCu3fD178Ou7dVwdlnw8svJ+ptxN20X5cxnjfhhs8wZqxj167IGvDl5fAMVhWs\nb0H4mCCYlVW7luCuXTlWhE1SEAy7hr7+uk1k2tD38bvusrDY0ktCNFd2ts1ieu21kc9tPB1SEQRa\nbd0a/xc+xgUdDKw/snORNSJERETkmBO3IOhN+Cf63ODHe++fD27zwEwgsdMxppGYIOicdfXcsoV1\n62x/n+rldqG4GLAgWF5u2RCwILhpE+zcydSp8MMfwpyH3rUQeM89iXwrcbN9O/gHHmS/a0XnO66t\nncXw7bdtW14Oz+YEg+nqmVYzJgiCBcFgjGBMRTDBA5/atbO2hbNwNhQEs7JsIplUcNtt8OijiXmt\nmIpgENJjguDjj8Of/pSYxhxDgiHHNmNM796p8+ESERGRFhfXBeacc9nAHOB44Dfe+xlRt+UCVwMN\nzTHXxjk3G6gGfuy9/0cDr3EjcCNAYWEh06dPb7k30EIqKyuPqF2rVhUAI3jllbls376DUW3bsnfx\nYqb65UA/9syYDMDMvXvZM306e/Z0Awbz9NMz6dt3D53372cYMOeJJ3hh7oeB/mz593P25K+9xszH\nHmNPGs/17j1885tD+W3FNDYMPoUVZWXU1ED79uN55ply+vVbwrx5A9mYX8Li6+9i+0knUVXnOFRX\nO+A03nlnBdOnr+LE3Fxyli5l585c9uxZw7rZs+man8+bb72V8PfXseMoli+3Rew3b36D6dOrE96G\nVLVrVz+2bevF9Omv03bNGk4FWLOG6dOnk7V3L+NuuYW9vXoxR4vaNVlNDaxdO5GamrXsmjuH6q5d\nmRfn36dH+rtRkkfHLP3omKUfHbP0k7bHzHsf9x+gAJgGDI3a9yDwi0Ye0zPY9gNWAv0P9zojR470\nqWjatGlH9Li5c70H7ydPDnaccYb348f7a67xvlcv7/0dd3jfpo33Bw54771/8027/3PPBfdftMh2\n/PGP/vrr7eI7g67yvksX71u18v7znz/at5ZU997rfQ77fXV2rvd33127/9xzvT/xRLt84YWRyw1p\n187+Kb333n/qU/5gcYkH73/0oyY+QZycd54ds86dk/LyKe3737d/m337vH3+e/f228Pj9MgjdmPP\nnkltY7rZuNH+2X79qxrvCwq8v+WWuL/mkf5ulOTRMUs/OmbpR8cs/aTaMQNm+yZktITMGuq93wFM\nByYBOOe+DXQF7mjkMeuD7fLgsUmeDiPxwq6htd0Wu3aFLVtYuhRKS4H5823Rthwr7Pbta3erHSfY\nr58N3nr/fZYutV09V8+AiRPh0kut+9zu3Ql6Ny2ruhq+9S24YcISsg8egKFDa28bMwbKyqzrYHl5\nZJ6dhhQURP0bFxbaegxELSaf4PGBoXDCGM3XcaiCAtvu3Il9/u+4g4J582xhxYceshs3bYrMGiSH\ntWaNbfvnb7EBmKWlyW2QiIiIxFU8Zw3t6pwrCC63Bc4GFjvnbgDOBa7y3tf7Lc0519E51zq43AUY\nDyyMV1tTVcwYQagdI1gbBOfNgxNPrL1/YaGt6VYbBFu1sjD4/vssWQJdKKdw93I49VS4+WbYtQv+\n+tcEvqOWM2+ejYX81Igy2xEVBM8/377/P/ZY04Jgx46xYwSz9u6hHZVJD4LhhDEKgoeKCYIAN9zA\ngbw8uOUWePNN+9wfPAiaQKbJwjUE+x7QjKEiIiKZIJ4VwR7ANOfcPGAWMNV7/xzwe6AQeCtYGuJb\nAM65Uc654E/5DAJmO+few7qU/th7n3FB8LjjLMvVBsGePWHrVmq2bGV4j81W8Rg2rPb+ztm8MbWL\nygMMHMjBRe+zcSOMZqbtO/VUGD/eZpd56aVEvZ0WFQ7ZG0KZVT2jZgQdPdrW1bvvPivuhUswNiQm\nCAaLyndjM50LDsLGjaoIpqD8fNuGM4cebNuedRddBHPn2uy6dwSdDTZtSk4D01BYEey5O+g+oIqg\niIjIMS1uk8V47+dRT3dO7329r+m9n02wlIT3/k1seYmM5lxkUXkAzjkHvvENzuMFRmTblPnRFUGw\n0LBoUdSOgQNxU/9LFge5oNtMDm7O4uCwkbRyzrqVhn1G08ybb1o+y189395069Yxt3/pS3DRRXa5\nKRXB2ipqsKh8IZvontXWSosKgiknuiK4fbv9AeT4/Ht4O/fv+PM/SuvwDyQbN8ZUi6Vha9bYH57y\nNiyx7rZpPJGUiIiIHF5CxgjKkYsJgqNGsbegOxfyLP33zLd9dYLgiSfa0oFVVcGO8ePJqtrHV/kR\np7WdQRlDWbfTZqKktNSCoE3Kk1befBPGjQNXVlbvF/0LLogUNJrVNTS6IliVnDUEQ6efDt/9rnV1\nlVjRFcEFC6CiAsrpxogDs7inx+8iy32Ei27KYa1da398cMuWWtfa3NxkN0lERETiSEEwxcUEwaws\nFva7gEm8SNdVs616FVSwQsOG2dCo2qrgRRcx/8RPcg/fYsCGV5nJaFavDm4rLbXJYtLsy/L69VbB\nmzhyNyxfXm8QzMqCO++0y3X+iQ4REwSD5QaGUkb+7uQGwdatbUKc445LysuntLAiuGMHLFtml3/0\no/ns6zuY1ZWdFASPQO0agkuWqFuoiIhIBlAQTHExQRCYlnchHagge8rkQ6qBEBkyOD8oGOIcvxz8\nB5bmDCZ7/z5mcGpsEIS06x4ajg88o3ChVTMb6Pp33XVw//1w3nmNP1/HjjbxzIEDQNeurOgxliuy\nniK3PLlBUBoW3TV02TIbJtq9+z46dLA5kMjLs8XQFQSbbM0a6FNUY78P1B9ZRETkmKcgmOI6dowN\ngv+sPIt9WW2t7+ewQ4dRlpbaOJ/aIAjMX96OH436OwfPPZ9/85G0D4JvvmnVshOqgxlD6/l3APt3\nuPXWw1fUOna0bTjxyBs9L+fEmnkwbZoN1Ay6i0rqaN/eqr5hRbCkBHJyPPn5wUyi4XHTZDFNUlMD\n69bBoPz1sHevgqCIiEgGUBBMcZ06Rboteg9lH7Tl/T7n2I56KoK5uTYHzLx5kccsWQJtRwwk+8V/\nU9OtRyQI9uljaSkNg+App0DO4jJbL6Nfv6N6vjAIhv/O/8m7zC5MnmxhIiducyrJEXKO2tC3dCkc\nf7ztr60IgnUPVUWwSTZtsrU5TzoQzCw8cGByGyQiIiJxpyCY4jp1sm6L+/fbkmg7dsCmsRfbjSef\nXO9jhg2LVATDx4R/4O/TB1atCu6YnW0hasmS+L6JFrRvH8yZA2PHYqvGDx5s7+Mo1A2CS3b3Ym77\nMTbYUt1CU1Z+fqQiGAbB2oogKAg2Q7h0xKi3f20DBSdMSG6DREREJO4UBFNcuKj89u2Rwt2Bq66B\nt9+utyIIFgTXr7cupeFjooNgbUUQIjOHpokPPrCxfCNGYGm3BZYGqBsEt2yBN3p+1K4oCKasggIL\ngbt2qSJ4tNauheHMpcv86fD5z6sKLiIikgEUBFNcGASjQ13pwCxbFL4B0RPGhJXBcDhgGARrV4wo\nLbVv0zU1Ld/4OAirmQPyN8GGDTB8+FE/Z90guHUrzO1/rvU/VBBMWfn58O67djn8fIcVQe+xILhl\nSzALkDTmgw/gi9yHb9cOPvOZZDdHREREEkBBMMVFB8EFC+wP9X37Nv6YsFD4+utwzz3We7J/f9vX\np4+tGFG7XEJpqfW3XLcuLu1vaeHC7/12Bgmgge6xzRE9WcyBA1ZROljYGf70J7j99qN+fomPggL7\n6EJsRfDAgWAdzXCSn/LypLQvnbz7/Aau4q+4T386MiWriIiIHNMUBFNcdBCcNg3GjDn8Os89etjj\n7rnHimaPPhoZRtenj20PO3PogQPw3nswZYoNUEwRK1fajKEFK4MgOGLEUT9n+L13+3arBgJ06HAA\nPvlJS9GSksLjlpVls4ZCZKH5nTvRWoJNVFUF57z+LXLcQbjttmQ3R0RERBJEQTDFhUHwgw9g9mw4\n66zDP8Y56x564ADcdReMHh25rbjYtvUGwXnzrFvYyJE2P/+IEXDJJfC3vx36Inv3RlJTAq1cae8h\na+67VhptgepFmzb2s327VV0Bevfee9TPK/EVhr4+feyPA2AVQQjGCSoINsni+17guoMPsfziL0VK\nqyIiInLMUxBMcWG3xWeesXFPTQmCABdcAOPHw7e/Hbs/rAiGXSwpKrIU9PTTNlPgU09Z+rz9dnji\nCVuYe+bMQ1/g9tttivkVK47kbR2xMAjy7rtw0kkt9rwdO1oQnDXLrg8cWNFizy3xEf4NIPxbBqgi\n2GzbttH3B9dTxhAK/3BPslsjIiIiCaQgmOLy863C98Yb0K5do3PExLjzTntMmzax+7t2tS/QixYF\nO7Ky2Ni+P7z8sn1xLiuDqVPhpz+Fq66yMXhz5hz6Ai+/bBXBSy6BPXuO6j02x8qVcELPXTbBTRyC\n4OzZNp4yL6+6xZ5b4iMMfdFFrJiKYDhGUIvKN+y73+W43eXcN+KP5HVpnezWiIiISAIpCKa4rCwL\nKd7DxIm2/vvRcA6GDIl0gQR4+cBpzGYk2//5GvTujffWFRWAUaNg7tzYmRc3boTly+G882wc4U03\nHd/9Cg0AACAASURBVF2jmmjPHpv345RW79mOOFUETzmlxZ5W4iisCEYHwZiKYNu2tkMVwfodPEjN\n357kGX8xxRcf/aRLIiIikl4UBNNAOE6wqd1CDycMgt7bDKJX77yfU5jF83OsgvLII9bdbuVKLAju\n2wcLFwLw+OMw85dv2RN985vwjW/An/9sYTHOwqUjBu0LJopp4SC4ZImNnRw1qsWeVuLosBVBsKqg\ngmD93n6brM2bmMLFnHNOshsjIiIiiaYgmAbCIHj22S3zfIMH2yykmzfbOoMeBzj++U8Lh/ffT6Qq\nGKai2bOpqoLPfQ4WPvSmlSZPPhm+8AW7/OijkRdYuDAua7eF4xr7bHkHunWz6VFbSMeOkRU0VBFM\nD2PHWlF6/PjIvpiKIGhR+Qbceiv86owpVNGKNzp8RJ95ERGRDKQgmAY6d4YuXSILxR+tIUNsu2CB\n9ewEOOccePFFG1cYFvfWr8cGzOXnw+zZvPwyVFZC6ZY3qR4xyqZq7NQJLrrIqoJVVbZ44ZAh8POf\nt0xjo4RBsOPKYKIY51rsucNJeZxrkaUJJQF69oTnn7fzI3RIRVBB8BB79sDDD3k+nj2FtQPO4q//\n7kBOTrJbJSIiIommIJgGvvlN65KZ1UJHK1wab+FCWzGiQwdbPqyiAm64wYZWQRAEnbPlJGbP5h//\ngFZUMYrZrOo1LvKE111nJcZnnoGbb7Z9Dz1kZcUWtHIldM7dRc7ShS2e1sIgOGiQrZwh6Sk31z6/\ntRXBnj1h7Vo4eDCp7Uolr70GA/fPo9e+5fT/8iV86EPJbpGIiIgkg4JgGhg7Fs4/v+Wer0cPm2gj\nrAieeKKNPzzuOBsn96lP2aoR69cHDxg1Cj9vHs//Yz+3f+gdWrOfN2qiguA550CvXtTceJM96ZVX\nWr/S119vuUYD6z7Yx79yLsJ5b+tjtKAwCKqLXPrr0CGqIjhqlJXAysqS2qZk8D4yrhaw7tqf/zzH\nffV2vp71I3xWFlx4YdLaJyIiIsmlIJiBwplDy8qsIjh8uFVRPvxhu/2mm6yQEh0E3f79dCsv46ri\n/wHw5OqxkSfMzmbladeSVVnB9okXwsMP27fxRx5puUZXV3Pjy1cwdu80eOwxS8ctSEHw2JGfH1UR\nHBf8weLNN5PWnmR54QXo1w8WLw52fPnLcP/9jH7vAS6veRI3caKNtRUREZGMpCCYoQYPhrfftu6g\nw4fbvm98A370I+sJWjcIAvzcfYkTZzzI1oJ+TJ3fnd27I8/3x3Y3M4WLePEj91tp8aqrbJH62tLM\nUXrpJSbueJYnT73XSpYtrKTEArK6yaW/mIpgSYmNE8zAIPj++1BTA6++ivUt/+Uvqbj+C3TwO/nL\nbTPgr39NdhNFREQkiRQEM9SQIVAdrJl+4om2HTkS7r7bLscEwZIS3mh7DsNaLSF7TyXbLvh/VFdb\nkAw9M7OIS5jC3K29bcenP21d8p58skXae2C+lTXWnnVtizxfXRMm2BjEMBRL+urQIaoi6JxVBTMw\nCG7YYNv5L2+2Mv8ZZ/D06P/jAK0Y/pnRFpBFREQkYykIZqhw5lDnYOjQQ2/v1cuCoPewbbtjwt6X\nePR7a2HdOgrv/yZZWZEhgJs3R2YfXbIkeIJTToHiYpg6tUXaW/nuUrbRke6DO7XI89XlHPTpE5en\nlgTLz69TiB43DpYvz7jZQ8M/5Oz43wKb0ffrX+c/L+fQs2fk/BcREZHMpSCYocKZQ0tLoV27Q2/v\n2RP277fJQMMxRuFjOnSAESOCLmfAtGm2LSqKCoLO2XoXixYddVu9h81vLmUppZSWHvXTyTEupiII\nkXGCb72VlPYkSxgEc9evBGB/zxKmTrWxwC248oqIiIikKQXBDNWjhy0BOGJE/bf37GnbdesiQfCE\nEyK3X3ABTJ9u3UP/+1+rwlx2mU0WWjtT/+DBlgzDPqhHwHu4/XZovWYZbkCpJnORwzqkInjyydCq\nVcZ1D92wwSr7xazCO8c/3+nN9u1wxRXJbpmIiIikAgXBDOUcTJkCP/xh/beHQXD9eguCrVvbvBuh\nO++0+9x6qwXB00+3NfiqqmDNmuBOgwZZWXH58iNu59e+Bg/8eh99WM0pVx6vSoYcVjhZTE1NsKN1\na5vwqG4QnDrVSt7HqPXr4SMfgb5uFbva9eR3D7eipCQyO7CIiIhkNgXBDDZxIvTvX/9t0UFw0SIY\nMACysyO35+XBz38O77xjk6ycfTa13TZru4eGfUmPsHvok0/Cj38Md1+2nCw8boD6hcrh5edbJTl6\nVlvGjYPZs2HrVrv+4IOWiO69NyltjLfduy0M9+sHQ/NWsvRAMdOmwY03QpZ+64uIiAgKgtKAHj1s\nG1YEo7uFhq64Ak47zS6fdZaFRYClS4M7hA9auLDZr//ee3Dddbacw9evWGY7NUBQmqBDB9vGjBO8\n9FLbjhkDf/gD3HKLXZ8xI6FtS5RwxtAePaDEreL9qhJycuycEhEREQEFQWlAmzY2hnDFCuvZWV8Q\ndA7++Ef47W/t9h49bOKZ2opghw42SOkIKoI/+5m1YfJkyF0ZJMvjjz/yNyQZIz/ftjHjBE89FaZN\n4+DOCvjsZ9lXMhCuvNKqhN4npZ3xFE4U07PwIB0r17CKYi6+WCtGiIiISISCoDSoZ0947TUba1Vf\nEARbcuHmmy0UOmdFu9qKIFj30CMIglu2WO4rLASWLbNU2ik+S0fIsSW6IjhlinWHBGDcOCbfNYv7\n+AL/vuV5K2Pv2GEzHB1jwopgn5z1ZB2sJqukmLvuSm6bREREJLUoCEqDevWyDAY270tTDBgQVREM\nH7hoUdTMHU2zYwcUFARXli5Vt1BpsuiK4O9+Z8MBwwrZy0t6cwf3sby6D7VT0M6alZyGxlH4frvv\nXwXA3b8rZtSoJDZIREREUo6CoDQonDAGIuP/Dqe01LqT7t8f7Bg82GauWLu2Wa99SBBUt1BporAi\nuHVrZKLQcM3Lt9+27aZN2GezTZtjNgi2aQN5Wy0Ixkz5KyIiIoKCoDQiDIJ9+tS/6Hx9Bgyw4t+K\nFcGOsJTYzAljaoPgvn22HoUqgtJEYUXw1VcjM4e+9hpUVEBZmV3ftAnIzYWTTrJxgseYDRtszK5b\ntdJ29OmT1PaIiIhI6lEQlAaFQbCh8YH1CSuHR7uExPbtQRBcvtwm81AQlCYKK4LPPWfbk06yUBjO\nC5OVFQRBsPUF33kHDh5MSlvjZf364PxdtQq6doXjjkt2k0RERCTFKAhKg8Ig2NTxgRAJgosXBzu6\ndLGfZgTBfftsYfqCAiIzz6hrqDRRXp5t16+3j82VV9rH79lnbf+4cVFB8JRTrGx4hGtdpqoNG4Lz\nd+VKdQsVERGReikISoOOpCLYqZN1SVuwIGrn4MGRPnlNsGOHbQsKiMzoqCAoTZSVFQmDEydG1rp8\n8EErLA8eXCcIAjO/8zz8+tfw1FOJb3AcrF8frAW6ahUUFye7OSIiIpKCFASlQSefDF/7WmQt7qYa\nOrRO7jvlFOt+VzuDTONiguDatTZAsWPH5jVCMlo4TnDCBPsct2tnhb8xY2xJkvLyoDfogAFUkMfo\nv38FbrsNrr/eytHN5T2sW9ei7+FI7d5tM6b27OFh9WoFQREREamXgqA0KCcHfvAD69nZHEOG2Nww\ntcOuxo2zL9fvvtukx4dBsGNH7Mt1z562SKFIE4XjBCdOtDlhxo+362EQrKmxtSp3VmRxFz/hwW5f\ng1/9Cior4ZVXmvdi3sNNN0FREfz0p0lfoD5cQ7DvcZusn7W6hoqIiEg9FASlxQ0dCnv3Rs0cOnas\nbcO5/A8jpiK4bp0taCjSDPn59rHp29euh91DTz3VgiBY99AVK+D33Mw3+AF85jNWOvznP5v3Yj/7\nmfU7HTQIvvIVe54kTj4TriFY4oKlI1QRFBERkXrELQg659o452Y6595zzi1wzn032N/XOTfDObfU\nOfekc65VA4//qnNumXPufefcufFqp7S8oUNtWztOsEcPq0ocSRBcv15BUJrt5pvhnnsiheTPfhZ+\n/3vrJlo3CIJ1Fa3OaQOTJtmsMjU1TXuhZ5+18HfFFTB/Ptx9Nzz8MLz4Ysu/qSYKK4K9dgdT96oi\nKCIiIvWIZ0WwCjjTez8cGAFMcs6NAX4C3Oe9LwW2A9fXfaBzbjBwJTAEmAT81jmXHce2SgsKV4yI\nGSc4bpwFwSZ0m6sNgvk+ah58kaa7+mr49Kcj1zt1st6bztUfBL0PJpD52McsSTVlbcGKCkuYI0bA\no49CdjZ89at227x5Lfp+miOsCHZ59yXr1x2ekCIiIiJR4hYEvakMruYGPx44E5gc7H8cuKieh38M\n+Jv3vsp7vwJYBoyOV1ulZeXlWRHikCC4fr0tDn8YtUHw4FYbW6iKoLSg7t1tGx0EATZuBD7yEQt0\nTeke+r3vwYYNrP7a76BtW9vXoQP07m2DZJNkxQrIa1dDq1dehHPPtfcjIiIiUkdOPJ88qOLNAY4H\nfgN8AOzw3lcHd1kL1PctvxfwdtT1hu6Hc+5G4EaAwsJCpk+f3iJtb0mVlZUp2a546tFjKDNmtGH6\ndKustM/NZRSw8KGH2HzmmY0+dt68fuTmFjH/P/9kNLBgxw7KE/zvl4nHLN019Zh5D61aTWDWrHWs\nWtWOrKxO1NQ4XnppPhVjtzJ82DBa/eUvzDrzzAZD1HGrVzPqvvv4V5dP8sn/N5Kn271O27Y2LvDE\n7t3JnTGDOUn6/Lz22gjO7/YObsUWFpaUsDmFP8c6z9KPjln60TFLPzpm6Sdtj5n3Pu4/QAEwDZgA\nLIva3xuYX8/9fwN8Kur6w8DHD/c6I0eO9Klo2rRpyW5Cwt19t/c5Od5XVQU7Dhzw/rjjvP/85w/7\n2Jtu8r5bN+/98897D97/739xbWt9MvGYpbvmHLPiYu+vvtr7wYO9P+kk+5g9+GBw42OP2Y5LL/V+\n7976n+CSS/z+dvm+K5s8eP/ww1G33XGH923aeF9dHfuY5cu9nzevGe+o+WpqvC8o8P7Zkd/x3jnv\ny8vj+npHS+dZ+tExSz86ZulHxyz9pNoxA2b7JmS0hMwa6r3fAUwHxgAFzrmwElkErK/nIWuxkMhh\n7icpauhQqK6GpUuDHTk5NmVjEyaM2bEjasZQUNdQaXGFhdYVdOXKyKS24SQrXHst/PznMHmyda3c\nty/2wdXV+JdeYnLuJ+g4oBsDB8JDD0XdPniwPWblytjHfepTcNZZTV5P80isWWPnz+itz9v51ty1\nX0RERCRjxHPW0K7OuYLgclvgbGARVhkMlyi/FqhvMM6zwJXOudbOub5AKTAzXm2VlhfOHBozTvC0\n02wtwcOME6wNguGsFz16xKWNkrkKC+2zuWcPDBxok8ls3Bh1hzvusNk/X3vtkPGC26a9h6us5Nkd\nE/jZz2y1iLfeihoWGE7OEj1OcPVq+yNIeXnzl6dohvfegy6U023VLDj//Li9joiIiKS/eFYEewDT\nnHPzgFnAVO/9c8BXgDucc8uAzli3T5xzFzrn7gHw3i8AngIWAi8Ct3rvk7cwlzTbwIE2vGr+/Kid\n11xjA7QeeaTRx8ZUBLt2hVb1rjAicsQKC6MWXu9rE8iE18vKbOkJf821dsPTT9c+7vLL4Z4Pvw5A\n67Mm8NGP2gylubmWG4FIEKxdP4XIc3TuDA88ELf3NW8efIo/47yH886L2+uIiIhI+ovnrKHzvPcn\nee9P9N4P9d6HIW+593609/547/1l3vuqYP+z3vtvRT3+B977/t77gd77F+LVTomPNm2sKvjGG1E7\n+/aFD3/Y+tE1suB2TBBUt1CJg3DmULCPZY8ekYrg738P3/42bN+VDZdcAs8/D7t3s2KF5blP9nmd\nqh4lPPyfIpyDbt1s1YnHHw96fYar2UdXBJ96yhYxvP12+O9/4YMPWv5NrVrFpN98lPu4A0aOtNcT\nERERaUBCxghKZpo0Cf73P9i1K2rnjTfC2rWNLri9fXtU11CtIShxEK4lCLbUSXRFcO5c265eDVx6\nKezdCy+8wLRpAJ6TKl+n9dkTYiYU/djHYOvWqHw3ZEikIrhyJcycaeXET38asrLqDCpsIV/4AoM2\nTuPxof9n3VCz9OtdREREGqZvChI3kybZhDGvvBK184IL7Ft4A93jvFdFUOIvDIJdukD79pGKYE2N\njbMDWLUKmDjRuic//TSvvALjOi8hZ1s5TJgQ83wlJVGPAeseumgR/mANy3/8lO27/HL7PH/0o/DY\nY/Zhb0E1i97nRX8uKz7+JXWnFhERkcNSEJS4GTfOFpePKf7l5sJ118Fzz9nEGXXs22fd6zrn7YfN\nmxUEJS7CINi3r227d7fP3jvvQGWl7Vu9Ghvoeskl+H//m7de3sM1/YK+znWCYHGxbWuD4JAhsHcv\ntw1/Ff+HP7C04JTIi515pqXObdta7g15j1+1ilUUM3x4yz2tiIiIHLsUBCVuWrWy2fJfeKFO8WPi\nRCu9LFt2yGN27LBtz6xgwJa6hkoc1A2C4cS0L0SNRl69Orhw2WW43bv59sbPMqnqH1YhHDgw5vl6\n9rQVUsLH7C6xCWPuW3AO3bK2cpf/SeQcKCqy7dq1LfeGtm4le98eBUERERFpMgVBiatJk+zL8eLF\nUTt7B0tE1rOMRBgEC6u1hqDETzhZTHRFECwI5uRAnz5RQfCMM3jvjC9wJX+jeN5z8KEPgXMxz5ed\nbfkurAguyR3CXtqwp/cJPPftWfxj5xmsWBHcOQ5BsPoDe+HNbYpru6mKiIiINEZBUOJq0iTbxnQP\nDb8INxIEu1QpCEr85OfD/ffb3EUQqQjOmAGDBsGAAVHdPLOy+F6n+zi951L8l++CL3+53ucsLo48\nZvH6fIawgLVTZjH4Y6WArTUItGgQ9B5+9zu45aOWWvue1kdzxIiIiEiT6CuDxFVxsX2xnjIlamd+\nvs3QUc8X4TAIdtwbLCavrqESJ7feCv362eWwIlhTAyedFFsRrKmB6dNh4IeLcT/9CYwdW+/z9ekT\nVRFcAitdP/oNacvQofZxrw2C3btbCfEog2BlJVxxBdxyCwzrYC/8vT8WH9VzioiISOZQEJS4u/FG\neP11Wz4NsG51vXs3WhHM27XOBhl26ZK4hkrGKiiA1q3t8ogRFuo2bICqKlsOcOtWOP30xp+juNgm\nuj1wwIJgcbGtp5mdDaNHRwXB7GwrQR5FEDx4EM44A/7+d/jpT+FzF6yC444jq2vnI35OERERySwK\nghJ3N99sX4rvvtuqK4B1j2skCLZbvdDuU2cslkg8OBepCoZBECzYzZxpl8eMafw5iovt871unQXB\nAQMit40da8tS7N4d7CgqOqoguGULzJ4N3/ue9VR1q1dZA3S+iIiISBMpCErctW4N3/8+zJkDTz8d\n7GykIljCClq//DxcdlliGyoZLRwnGB0EV6+2cYMFBVBa2vjjo5eQqBsEx4yxKt7s2cGOoiL82rXM\nm3dkba2oqH2ayIsWq1uoiIiINJ2CoCTEJz4Bw4fDt78d7Ojd29ZSO3Ag5n47dsAXsn+Ny8qCz30u\n8Q2VjNWnj40Z7NgxEgRXrbKK4CmncNhJWMIcNmsW7Np1aBCE2AljDq5cw/DhPnZG3SYKg2BeHpGG\nKgiKiIhIMygISkJkZcHFF8P770N1NRYEvYf162Put2/zLj5d85BVA2vLHSLx97Ofwb/+ZZfDFU4W\nL4b58+HUUw//+PAxU6faNjoIduliFcXoIJizbzcd2BVZVqIZYoLg7t02iFFBUERERJpBQVASpnMw\nj8W2bTS4hMTJcx8hz1fAF7+Y2MZJxuvdGwbbOvC0aWOLzk+ZYl06mxIE27aFbt3gtdfsenQQBOty\nWlv9Cz7/Raxlw4bmt7Wy0rZ5eUSmKlUQFBERkWZQEJSECYPg1q00uKj8hz54jHntx1pfPJEk6tPH\nKthgs342RXEx7NtnE96G3UtD3bpBeXlwJSoIbtzY/LbFVATDIFj3BUVEREQaoSAoCdOpk223bSMS\nBIOZE+++Gy6/aD99KhawsNvpSWmfSLQwV5WUWIhrirAod/zxtkpEtK5dYfv2YFhsVBDctK4a7r03\nsnBhE9QbBFURFBERkWZQEJSEiakI5uVBhw6wZg2bN8NPfgLb3l5CLtV0PWNYUtspApEg2NRqIESy\nWN1uoWBBEILPf48e1OAoYi3d574Id94J550HO3c26XViguDq1ZCTAz17Nr2hIiIikvEUBCVhYoIg\n1C4hMX++Xf3FDWUAnHX70MQ3TqSOMAg2ZXxg3cc0FgTLy4FWrdiWW0gRaxm+bDIcd5ytOXH55cFs\nSo0Lg2D79lhFsKjo0BKkiIiISCMUBCVh6g2Ca9dSZvmPvpXzrbIxcGBS2icSLQxzH/pQ0x/TlIpg\nOE5wvSuiH8sZv+WfcOml8Pvfw0svwa9+ddjXqaiwyWlyctDSESIiInJEFAQlYfLy7Itr3YpgWZl9\nSW63osy+QbdqldR2igBMmmRrCDana+iYMXb/M8889La6QXBVTRGnM538mh34j18K119v58TcuYd9\nnYqKqDUE16zRRDEiIiLSbDnJboBkDuesKlgbBIuKYNMm3p9XxdChraGsTLOFSsrIymr+x7GwEGbM\nqP+26CC4fz+srC4imxp2kYcbew55UFslP5yYIFhe3vTZbEREREQCqghKQsUEwWDm0O1l6zh54G5Y\nvhyGanygHJs6d7Y/hpSX2zmwFps59F9cwIbtbexORUXNC4J79sDevZF+1yIiIiJNpCAoCVVfEOy0\nZw3jCxbYPgVBOUZlZ9sSKuXlsGULrME+/5O5NLKWYFGRdfX0vtHnqg2C4cnUpUv8Gi4iIiLHJAVB\nSaiYIDh4MDXZOVzJ3xiWFcwYoyAox7CuXSMVwWe5kFc//kue46Ns2BDcoXdvW5F+27ZGn6c2CG7Z\nYjsUBEVERKSZFAQloWKCYM+evDP6Zj7Dg5TMfNqmQezXL6ntE4mnMAhu2QK7aY//3G1UkxtbEYTD\ndg+tqAiWjghPJnUNFRERkWZSEJSECoNg2PPtoZ7fYo9rR85/X4QhQ2yGDpFjVHQQBCgthdxcIhXB\nZgRBVQRFRETkaOhbtyRU5842Y+Lu3Xb9raVdeGbAV+2KuoXKMa5uEOzSBbp359CK4Jo1jT6PgqCI\niIgcLS0fIQkVvah869aweDEsvfV2KPovXHRRchsnEmddu9pnv7zcglzr1tCjR1RFsEcPm1WmkYpg\nTQ1UVtYJgp06xb3tIiIicmxREJSECr+vbt1qM9/v3w+DTm4L9/43uQ0TSYCuXS3ILVkS+aNI9+6w\ncmVwh+xsC4ONBMGwmp6XB6zeCgUFkKNf5SIiItI86hoqCRV++d22zdaPB/UIlcwRLiq/eHGkN2dM\nRRAiS0g0oKLCtrUVQXULFRERkSOgICgJFd01dP58mxvmhBOS2yaRRAmD4MqVsUFwyxY4cCC4U+/e\njVYEKyttqyAoIiIiR0NBUBIqOgiWldmsiW3aJLdNIokSBkGI7RrqPWzeHNxQVGRBsIFF5WMqglu3\naukIEREROSIKgpJQ0WMEy8rULVQyS3QQjK4IQp0lJPbsgR076n0OdQ0VERGRlqAgKAnVqpV9gV27\nFpYtUxCUzBKd2cLL3bvbtqlLSCgIioiISEtQEJSE69wZ3njDer4NG5bs1ogkTqtWkJ9vl8Menb16\n2bZ2WGDv3nV2xAqDYIecPbB3r7qGioiIyBFREJSE69wZFi60y6oISqYJu4dGdw3NzYVVq4I7hBXB\nwwXBA1tjn0hERESkGRQEJeHCAkbr1tC/f3LbIpJodYNgVpYVAWuDYI8etvMwQTCvakvsE4mIiIg0\ng4KgJFwYBAcN0jrYknnCIBjdo7OkJGpR+ZwcC4OHGSPYdo8qgiIiInLkFAQl4cIvwOoWKpmobkUQ\noLg4qiIY7vjgg3ofX1EB7dtD1ragIqgxgiIiInIEFAQl4cLvrZooRjJRYSE4d2hFcP16qKoKdowa\nBXPmRK0yHxEGQbaoa6iIiIgcubgFQedcb+fcNOfcIufcAufc7cH+J51zc4Oflc65uQ08fqVzbn5w\nv9nxaqckniqCksluvhmeesrGyIaKi21b2xt07FhbS3DevEMeX1ERtZg8RBbnFBEREWmGeI7Qqgbu\n9N6/45zLA+Y456Z6768I7+Cc+zmws5HnOMN7vyWObZQkOPFE++46alSyWyKSeEVFcOmlsftKSmy7\nciUcfzwwbpzteOstGDky5r61QXDLFigo0EBbEREROSJxqwh67zd4798JLlcAi4Be4e3OOQdcDvw1\nXm2Q1HTaaVbM6NYt2S0RSQ1hRbB2nGDv3tCzpwXBOmKCoLqFioiIyBFKyJ+SnXMlwEnAjKjdE4BN\n3vulDTzMAy855zzwB+/9Aw08943AjQCFhYVMnz69hVrdciorK1OyXdIwHbP0k87H7OBBR1bWRF59\ndRX9+68EYHBpKXmvvMKMOu9pw4aRdO1axbZly8hu1Yp30/Q9Q3ofs0ylY5Z+dMzSj45Z+knXYxb3\nIOicaw/8HfiC935X1E1X0Xg1cLz3fr1zrhsw1Tm32Hv/Wt07BQHxAYBRo0b5008/veUa30KmT59O\nKrZLGqZjln7S/ZjZOvIlnH56ie248EK4805OP+EE6N699n7eQ0lJHp0WH4S+fdP6Paf7MctEOmbp\nR8cs/eiYpZ90PWZxnTXUOZeLhcC/eO+fidqfA1wCPNnQY73364PtZmAKMDqebRURSaaYtQTBJoyB\nSPfQYAZRdQ0VERGRlhDPWUMd8DCwyHt/b52bzwYWe+/XNvDYdsEEMzjn2gEfBsri1VYRkWQ7ZC3B\nk0+GVq3guefg8suhY0eYPFlBUERERFpEPLuGjgeuBuZHLRHxNe/988CV1OkW6pzrCTzkvT8fKASm\nWJYkB3jCe/9iHNsqIpJUxcWwdq0V/nJzsfUlRo6ERx6xHaWlcNllfIcvc8arlbB3rxaTFxERrJEk\ndgAACX9JREFUkSMWtyDovX8DcA3c9v/q2bceOD+4vBwYHq+2iYikmpISqKmBdesiy0lwzTW2+vz9\n98OgQey/9jPc9dT/cWDecXDxxXDFFY08o4iIiEjD4jpGUEREmiZcQiJmnOBnPwv/+x+cdBK0acPm\nn/2RwSzgT/dugWeegX79ktFUEREROQYoCIqIpICwChg9TvDJJ+GLX4xcr6h0LGIwx3Vum9C2iYiI\nyLFHQVBEJAX07m3bZctse/Ag3H03/OIXsGKF7auosG1eXuLbJyIiIscWBUERkRTQujVMnAiPPQZV\nVfDSS5FuopMn2/bVV21bWpqMFoqIiMixREFQRCRFfPObNnPoo4/CH/4A3brBiBHw1FM2kcwDD8CE\nCTBgQLJbKiIiIukunstHiIhIM5x1lq0jf889sGkT3HWXLR/4la/YKhLLlsF3v5vsVoqIiMixQBVB\nEZEU4Rx861uwYYNVAD/zGbjsMrvtttts2cCPfzy5bRQREZFjgyqCIiIp5Nxzrftn586R1SFGjoQ5\nc+CWW2wsoYiIiMjRUhAUEUkhzsErr9g29IlPwNy5cOONyWuXiIiIHFvUNVREJMXk5EB2duT6bbfB\n4sWaJEZERERajoKgiEiKy8mB449PditERETkWKIgKCIiIiIikmEUBEVERERERDKMgqCIiIiIiEiG\nURAUERERERHJMAqCIiIiIiIiGUZBUEREREREJMMoCIqIiIiIiGQYBUEREREREZEMoyAoIiIiIiKS\nYRQERUREREREMoyCoIiIiIiISIZREBQREREREckwCoIiIiIiIiIZRkFQREREREQkwygIioiIiIiI\nZBjnvU92G1qMc64cWJXsdtSjC7Al2Y2QZtExSz86ZulHxyz96JilHx2z9KNjln5S7ZgVe++7Hu5O\nx1QQTFXOudne+1HJboc0nY5Z+tExSz86ZulHxyz96JilHx2z9JOux0xdQ0VERERERDKMgqCIiIiI\niEiGURBMjAeS3QBpNh2z9KNjln50zNKPjln60TFLPzpm6Sctj5nGCIqIiIiIiGQYVQRFREREREQy\njIKgiIiIiIhIhlEQjCPn3CTn3PvOuWXOubuT3R6pn3NupXNuvnNurnNudrCvk3NuqnNuabDtmOx2\nZjLn3CPOuc3OubKoffUeI2d+FZx385xzJyev5ZmrgWP2HefcuuBcm+ucOz/qtq8Gx+x959y5yWl1\nZnPO9XbOTXPOLXLOLXDO3R7s17mWoho5ZjrXUpRzro1zbqZz7r3gmH032N/XOTcjOM+edM61Cva3\nDq4vC24vSWb7M1Ejx+wx59yKqPNsRLA/bX43KgjGiXMuG/gNcB4wGLjKOTc4ua2SRpzhvR8RtQbM\n3cDL3vtS4OXguiTPY8CkOvsaOkbnAaXBz43A7xLURon1GIceM4D7gnNthPf+eYDgd+OVwJDgMb8N\nfodKYlUDd3rvBwFjgFuDY6NzLXU1dMxA51qqqgLO9N4PB0YAk5xzY4CfYMesFNgOXB/c/3pgu/f+\neOC+4H6SWA0dM4AvR51nc4N9afO7UUEwfkYDy7z3y733+4G/AR9Lcpuk6T4GPB5cfhy4KIltyXje\n+9eAbXV2N3SMPgb80Zu3gQLnXI/EtFRCDRyzhnwM+Jv3vsp7vwJYhv0OlQTy3m/w3r8TXK4AFgG9\n0LmWsho5Zg3RuZZkwflSGVzNDX48cCYwOdhf9zwLz7/JwFnOOZeg5gqNHrOGpM3vRgXB+OkFrIm6\nvpbGfzlL8njgJefcHOfcjcG+Qu/9BrD/aIFuSWudNKShY6RzL7V9Lugq80hUl2sdsxQTdD87CZiB\nzrW0UOeYgc61lOWcy3bOzQU2A1OBD4Ad3vvq4C7Rx6X2mAW37wQ6J7bFUveYee/D8+wHwXl2n3Ou\ndbAvbc4zBcH4qe+vNVqrIzWN996fjJXyb3XOTUx2g+So6NxLXb8D+mNdazYAPw/265ilEOdce+Dv\nwBe897sau2s9+3TckqCeY6ZzLYV57w9670cARVhFdlB9dwu2OmYpoO4xc84NBb4KnACcAnQCvhLc\nPW2OmYJg/KwFekddLwLWJ6kt0gjv/fpguxmYgv1S3hSW8YPt5uS1UBrQ0DHSuZeivPebgv9Ma4AH\niXRJ0zFLEc65XCxQ/MV7/0ywW+daCqvvmOlcSw/e+x3AdGx8Z4FzLie4Kfq41B6z4PZ8mt7tXlpY\n1DGbFHTN9t77KuBR0vA8UxCMn1lAaTALVCtscPazSW6T1OGca+ecywsvAx8GyrBjdW1wt2uBfyan\nhdKIho7Rs8A1waxdY4CdYbc2Sa46YyQuxs41sGN2ZTA7Xl9sgP3MRLcv0wXjjh4GFnnv7426Seda\nimromOlcS13Oua7OuYLgclvgbGxs5zTg0uBudc+z8Py7FHjFe5+S1aVjVQPHbHHUH8gcNqYz+jxL\ni9+NOYe/ixwJ7321c+5zwH+AbOAR7/2CJDdLDlUITAnGXecAT3jvX3TOzQKecs5dD6wGLktiGzOe\nc+6vwOlAF+fcWuDbwI+p/xg9D5yPTYKwB7gu4Q2Who7Z6cH02h5YCdwE4L1f4Jx7CliIzYJ4q/f+\nYDLaneHGA1cD84OxMABfQ+daKmvomF2lcy1l9QAeD2ZrzQKe8t4/55xbCPzNOfd94F0s4BNs/+Sc\nW4ZVAq9MRqMzXEPH7BXnXFesK+hc4LPB/dPmd6PTHxVEREREREQyi7qGioiIiIiIZBgFQRERERER\nkQyjICgiIiIiIpJhFARFREREREQyjIKgiIiIiIhIhtHyESIiIg1wznUGXg6udgcOAuXB9T3e+3FJ\naZiIiMhR0vIRIiIiTeCc+w5Q6b3/WbLbIiIicrTUNVREROQIOOcqg+3pzrlXnXNPOeeWOOd+7Jz7\npHNupnNuvnOuf3C/rs65vzvnZgU/45P7DkREJJMpCIqIiBy94cDtwDDgamCA93408BDw+eA+vwTu\n896fAnw8uE1ERCQpNEZQRETk6M3y3m8AcM59ALwU7J8PnBFcPhsY7JwLH9PBOZfnva9IaEtFRERQ\nEBQREWkJVVGXa6Ku1xD5vzYLGOu935vIhomIiNRHXUNFREQS4yXgc+EV59yIJLZFREQynIKgiIhI\nYtwGjHLOzXPOLQQ+m+wGiYhI5tLyESIiIiIiIhlGFUEREREREZEMoyAoIiIiIiKSYRQERURERERE\nMoyCoIiIiIiISIZREBQREREREckwCoIiIiIiIiIZRkFQREREREQkw/x/L0ju8W/TkR4AAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63d0cacfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Red - Predicted Stock Prices  ,  Blue - Actual Stock Prices\")\n",
    "plt.rcParams[\"figure.figsize\"] = (15,7)\n",
    "plt.plot(testY , 'b')\n",
    "plt.plot(pred , 'r')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Prices')\n",
    "plt.title('Check the accuracy of the model with time')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "813fd442e6cfdd9945eb3bc1ae69561bd1ebaca7",
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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